
Citation: Luís J. del Valle, Lourdes Franco, Ramaz Katsarava, Jordi Puiggalí. Electrospun biodegradable polymers loaded with bactericide agents[J]. AIMS Molecular Science, 2016, 3(1): 52-87. doi: 10.3934/molsci.2016.1.52
[1] | Lei Chen, Ruyun Qu, Xintong Liu . Improved multi-label classifiers for predicting protein subcellular localization. Mathematical Biosciences and Engineering, 2024, 21(1): 214-236. doi: 10.3934/mbe.2024010 |
[2] | Hangle Hu, Chunlei Cheng, Qing Ye, Lin Peng, Youzhi Shen . Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification. Mathematical Biosciences and Engineering, 2024, 21(1): 369-391. doi: 10.3934/mbe.2024017 |
[3] | Jiyun Shen, Yiyi Xia, Yiming Lu, Weizhong Lu, Meiling Qian, Hongjie Wu, Qiming Fu, Jing Chen . Identification of membrane protein types via deep residual hypergraph neural network. Mathematical Biosciences and Engineering, 2023, 20(11): 20188-20212. doi: 10.3934/mbe.2023894 |
[4] | Yanping Xie, Zhaohui Dong, Junhua Du, Xiaoliang Zang, Huihui Guo, Min Liu, Shengwen Shao . The relationship between mouse lung adenocarcinoma at different stages and the expression level of exosomes in serum. Mathematical Biosciences and Engineering, 2020, 17(2): 1548-1557. doi: 10.3934/mbe.2020080 |
[5] | Cicely K. Macnamara, Mark A. J. Chaplain . Spatio-temporal models of synthetic genetic oscillators. Mathematical Biosciences and Engineering, 2017, 14(1): 249-262. doi: 10.3934/mbe.2017016 |
[6] | Hacı İsmail Aslan, Hoon Ko, Chang Choi . Classification of vertices on social networks by multiple approaches. Mathematical Biosciences and Engineering, 2022, 19(12): 12146-12159. doi: 10.3934/mbe.2022565 |
[7] | Yongyin Han, Maolin Liu, Zhixiao Wang . Key protein identification by integrating protein complex information and multi-biological features. Mathematical Biosciences and Engineering, 2023, 20(10): 18191-18206. doi: 10.3934/mbe.2023808 |
[8] | Wenjun Xu, Zihao Zhao, Hongwei Zhang, Minglei Hu, Ning Yang, Hui Wang, Chao Wang, Jun Jiao, Lichuan Gu . Deep neural learning based protein function prediction. Mathematical Biosciences and Engineering, 2022, 19(3): 2471-2488. doi: 10.3934/mbe.2022114 |
[9] | Feng Wang, Xiaochen Feng, Ren Kong, Shan Chang . Generating new protein sequences by using dense network and attention mechanism. Mathematical Biosciences and Engineering, 2023, 20(2): 4178-4197. doi: 10.3934/mbe.2023195 |
[10] | Kunli Zhang, Shuai Zhang, Yu Song, Linkun Cai, Bin Hu . Double decoupled network for imbalanced obstetric intelligent diagnosis. Mathematical Biosciences and Engineering, 2022, 19(10): 10006-10021. doi: 10.3934/mbe.2022467 |
Protein is a major component for almost all living creatures. It is highly related to the maintenance of normal physical functions in cells [1]. Several complicated and essential biological processes need proteins to participate in, such as cell proliferation [2], DNA replication [3], enzyme-mediated metabolic processes [4], etc. Furthermore, protein provides important contributions to construct basic cellular structure, maintain cellular microenvironment and form complex macrostructures. Thus, the research of protein-related problems is quite hot in recent years. Determination of the functions of proteins is one of the essential problems. Experimental determination is a solid method. However, it also has some evident shortcomings, such as high cost and low efficiency. Thus, it is of great urgency to design novel methods with low cost and high efficiency.
In recent years, several computational methods have been designed to identify protein functions. Most of them are data-driven methods. Based on lots of proteins with annotated functions, which can be obtained from some public databases, models were set up by using some existing or newly designed computer algorithms. The basic computational method to identify protein functions is based on protein sequence similarity measured by BLAST [5]. Other methods, such as sequence motif based methods (PROSITE) [6], profile-based methods (PFAM) [7], structure-based methods (FATCAT and ProCAT) [8], were also proposed to identify protein functions. In recent years, network-based methods become more and more popular to tackle some protein-related problems. Two previous studies employed protein network information to design hybrid approaches for the identification of protein functions. The method that adopted protein network information is an important step to identify protein functions [9,10]. Other steps used methods based on protein sequence similarity or biochemical and physicochemical description of proteins. Most established methods always focused on proteins, analyzing their sequences, properties, etc. Few studies considered function labels. As inspired by some studies on drug-related problems [11,12], which considered label information and improved the performance of classifiers, the associations of function labels may also be important information for protein function identification.
In this study, we constructed a multi-label classifier with a label space partition to identify protein functions. To conduct this investigation, we selected proteins of mouse, one of the most extensively studied organisms, as the research object. Proteins and their function annotations were retrieved from MfunGD [13]. 24 functional types were reported in such database. A label space partition method, incorporating Louvain method [14], was applied to analyze the associations of 24 functional types, resulting in some subsets of types. To prove such partition can improve the performance of classifiers, we set up several classifiers with RAndom k-labELsets (RAKEL) [15], with support vector machine (SVM) [16] or random forest (RF) [17] as the base classifier. On each type subset, a multi-label classifier was set up and they were integrated in the proposed classifiers. The results indicated that classifiers with a label space partition were always superior to those without considering the partition of functional types. Furthermore, these classifiers also provided better performance than those with a random partition of functional types.
We sourced the mouse proteins and their functional types from one previous study [9]. This information was retrieved from MfunGD (http://mips.gsf.de/genre/proj/mfungd/) [13], a public database collecting annotated mouse proteins and their occurrence in protein networks. In such database, mouse proteins were classified into 24 types, which are illustrated in Figure 1. The types of each mouse protein were determined by manually checking its annotation in the literature and GO annotation [18,19]. Because we encoded mouse proteins according to their functional domain or interaction information, those without these two types of information were excluded. Finally, a dataset consisting of 9655 mouse proteins were constructed. These proteins were also classified into above mentioned 24 functional types. The number of proteins in each functional type is also shown in Figure 1. It was easy to obtain that the sum of protein numbers in all 24 types were 29850, which was much larger than the number of different mouse proteins (9655). This fact implied that several proteins belonged to two or more functional types. Determination of functional types of mouse proteins was evidently a multi-label classification problem if functional types were deemed as labels.
As mentioned above, mouse proteins in MfunGD were classified into 24 functional types and assigning these types to given proteins was a multi-label classification problem, where types were termed as labels. Due to the number of labels, it was difficult to directly build powerful multi-label classifiers. The partition of label set may be helpful to optimize classifiers as inspired by some studies on drug-related problems [11,12]. Thus, this section proposed a label space partition method to divide labels into some label subsets.
To implement this method, a label network was constructed first. Given a training dataset D with h labels (h = 24 in this study), denoted by $ {l}_{1}, {l}_{2}, \dots, {l}_{h} $, the label set for one sample s was defined as L(s). For each label $ {l}_{i}(1\le i\le h) $, samples having such label constituted a sample subset, denoted as $ {SL(l}_{i}) $, that is
$ {SL(l}_{i}) = \left\{s:s\in D \ and \ {l}_{i}\in L\left(s\right)\right\} $ | (1) |
The label network defined labels as nodes and two nodes were connected by an edge if and only if their corresponding labels, say $ {l}_{i} $ and $ {l}_{j} $, had common samples, that is $ {SL(l}_{i})\cap {SL(l}_{j})\ne \varnothing $. Furthermore, a weight was assigned to each edge for indicating the different association strength of labels. For an edge e, its weight was defined by
$ w\left(e\right) = \left|{SL(l}_{i})\cap {SL(l}_{j})\right| $ | (2) |
where $ {l}_{i} $ and $ {l}_{j} $ were the endpoints of edge e. For an easy description, let us denoted such label network by NL.
The Louvain method [14], a community detection algorithm, was performed on the label network NL to classify labels into some subsets. Such method adopts a greedy aggregation scheme to detect communities such that nodes in each detected community have strong associations. Initially, each node in the network constitutes a community. A loop procedure is executed. In each round, two communities are selected and merged when such merging can provide highest contribution to modularity. For a node n and community C, the gain in modularity, denoted by $ \varDelta Q $, by merging n and C is defined as
$ \varDelta Q = \left[\frac{{\Sigma }_{in}+{k}_{n, in}}{2m}-(\frac{{\Sigma }_{tot}+{k}_{n}}{2m}{)}^{2}\right]-[\frac{{\Sigma }_{in}}{2m}-(\frac{{\Sigma }_{tot}}{2m}{)}^{2}-\left(\frac{{k}_{n}}{2m}{)}^{2}\right] $ | (3) |
where $ {\Sigma }_{in} $ stands for the overall weights of edges inside C, $ {\Sigma }_{tot} $ stands for the overall weights of edges adjacent to nodes in C, $ {k}_{n, in} $ represents the overall weights of edges connecting n and nodes in C, $ {k}_{n} $ denotes the overall weights of edges adjacent to n, m is the overall weights of edges in the network. For each node n, the gain in modularity by merging it and each of its neighbor is computed. The merging producing the highest gain in modularity is selected and a new network is constructed. In details, if such merging involves node n and community C, the new network combines n and community C, producing a new node $ {n}^{'} $. The weight of an edge connecting $ {n}^{'} $ and another node $ {n}^{''} $ in the network is updated as the overall weights of edges connecting n (C) and $ {n}^{''} $. In the next round, above procedure is executed on the new network. The loop stops until the gain in modularity cannot be positive. The remaining communities in the network indicate a label partition.
In this study, the Louvain method was performed on the label network NL. By refining its outcome, we can access a label partition. Let us denote the label partition as $ {L}_{1}, {L}_{2}, \dots, {L}_{t} $.
Efficient classifiers always adopt informative features of samples, which contain essential properties of samples as much as possible. This study employed two schemes to encode each mouse protein. The first scheme extracted features derived from functional domain information of proteins through a natural language processing approach, whereas the second one generated features from several protein-protein interaction (PPI) networks. Their descriptions were as below.
Functional domain information is deemed to be useful to investigate various protein-related problems [20,21,22,23,24]. Here, we also adopted such information to encode each mouse protein.
We retrieved the functional domain information of all mouse proteins from InterPro database (http://www.ebi.ac.uk/interpro/, accessed in October 2020) [25]. This information contained 48739 mouse proteins, covering 16797 domains. Each domain was termed as words, whereas mouse proteins, annotated by domains, were deemed as sentences. Then, such above information was fed into the well-known natural language processing approach, word2vec [26,27], to learn embedding features of domains. As a result, each domain was encoded by a 256-D feature vector. Here, the word2vec program retrieved from https://github.com/RaRe-Technologies/gensim was adopted. It was executed with its default parameters.
The feature vectors of domains were further refined to represent each mouse protein. For each mouse protein, it was encoded by a vector, which was defined as the average of vectors of domains that were annotated on such protein. Thus, each protein was also represented by 256 features. For convenience, such obtained features were called domain embedding features.
Network has been deemed to be a popular research form because it can organize objects at a system level. However, a gap exists between network and traditional machine learning algorithms. This gap promotes the process of network embedding algorithms, which can abstract linkage in one or more networks and learn features for each node in the network(s). In recent years, several network embedding algorithms, such as DeepWalk [28], Node2vec [29], and Mashup [30], etc. have been proposed. Some of them have been applied to tackle different protein-related problems [30,31,32,33,34]. Features obtained by network embedding algorithms are quite different from those extracted from inherent properties of samples and can reflect different aspects of samples. Here, we adopted Mashup to extract features of mouse proteins from several PPI networks.
We used the mouse PPI information collected in STRING (https://www.string-db.org/, Version 10.0) [35], a public database containing interaction of 9, 643, 763 proteins from 2031 organisms. Interactions in this database are derived from five main sources: Genomic context predictions, High-throughput lab experiments, (Conserved) Co-expression, Automated textmining, Previous knowledge in databases. Accordingly, they can widely evaluate the associations of proteins. The mouse PPI information involves 20648 mouse proteins and 5, 109, 107 interactions. Each interaction is assigned eight scores, where the first seven scores measure the association of proteins from some aspect of proteins and they are integrated in the last score. For each of first seven scores, a PPI network was constructed, where proteins were defined as nodes and two nodes were connected by an edge when their corresponding proteins can constitute a PPI with such score larger than zero. In addition, this score was assigned to the edge as its weight. Accordingly, seven PPI networks were built, which can be used to extract informative features of mouse proteins.
The network embedding algorithm, Mashup [30], was executed on above constructed seven PPI networks. To our knowledge, it is the only network embedding algorithm that can process multiple networks. This method contains two stages to extract features for each node. In the first stage, each node in each network is assigned a raw feature vector on the basis of random walk with restart algorithm [36,37]. In this way, several raw feature vectors are produced for the same node. It is necessary to combine them into one vector. At the same time, the dimensionality reduction is also inevitable because of the high dimension of raw feature vectors, which is equal to the node number in the network. All these are done in the second stage. It supposes a uniform vector for each node and a context vector for any node in any network. Based on them, it produces an approximate vector for any node in any network. The optimal components in above two types of vectors were determined by solving an optimized problem such that the produced approximate vectors based on them should be approximate to raw feature vectors as much as possible. For details, please refer to reference [30].
This study adopted the Mashup program downloaded from http://cb.csail.mit.edu/cb/mashup/. Likewise, it was executed with the default parameters. For the dimension of feature vectors, we tried various values between 100 and 300. For convenience, features produced by Mashup were called network embedding features.
Accordingly, each mouse protein can be represented by three forms: (1) domain embedding features; (2) network embedding features; (3) domain and network embedding features.
As mentioned in Section 2.1, several mouse proteins belonged to two or more functional types. A natural way to assign types to given proteins is to design a multi-label classifier. Generally, there are two schemes to construct multi-label classifiers: problem transformation and algorithm adaption [38]. The former one transforms the original multi-label classification problem into some single-label classification problems. The later one generalizes the single-label classification algorithm so that it can process samples with more than one labels. Here, we adopted a widely used problem transformation method, called RAKEL [15], to construct the multi-label classifier.
RAKEL is a generalized method of label powerset (LP) algorithm. Given a dataset with h labels, say $ {l}_{1}, {l}_{2}, \dots, {l}_{h} $, randomly construct m label subsets, each of which consists of k labels. For each of these label subsets, new labels are defined as the members in its power set. These new labels are assigned to samples based on their original labels. After such operation, each sample is assigned only one new label. Samples with their new labels constitute a new dataset. A classifier is set up by training some single-label classification algorithm on such new dataset. Accordingly, m classifiers can be set up, which are integrated in RAKEL. For a query sample $ x $, each classifier gives a binary prediction result (0 or 1) for each label $ {l}_{i} $. RAKEL calculates the average vote rate for each label $ {l}_{i} $. When the average vote rate is greater than a given threshold (Generally, it is set to 0.5), $ {l}_{i} $ is assigned to $ x $. For an easy description, classifiers built by RAKEL were termed RAKEL classifiers in this study. To quickly implement RAKEL, the tool "RAKEL" in Meka (http://waikato.github.io/meka/) [39] was directly employed. The main parameters of RAKEL, m and k, were tuned in this study.
As mentioned in Section 2.2, all labels can be divided into t partitions, say $ {L}_{1}, {L}_{2}, \dots, {L}_{t} $. For each partition, a new dataset is constructed by restricting labels of each sample into this partition. For instance, if one sample is assigned three labels, say $ {l}_{1}, {l}_{2}, {l}_{3} $ and $ {l}_{1}, {l}_{3} $ belongs to one partition, this sample is assigned $ {l}_{1}, {l}_{3} $ as its labels in the new dataset. Accordingly, a RAKEL classifier is built on the new constructed dataset. The final classifier integrates these RAKEL classifiers by collecting their results. In detail, for a query sample, each RAKEL classifier yields its prediction (i.e., a label subset). The final prediction is the union of label subsets yielded by all RAKEL classifiers.
When building the RAKEL classifiers, a single-label classification algorithm is needed. In this study, two powerful classification algorithms were employed: SVM [16] and RF [17].
SVM is a popular classification algorithm based on statistical learning theory [31,34,40,41,42,43,44,45,46]. Its principle is to use a kernel function to map samples from the original space to a higher-dimensional feature space so that samples are linearly separable in the new space. So far, several types of SVM have been designed to process different problems. Here, one type of SVM was adopted. The sequential minimal optimization (SMO) algorithm [47] was employed to optimize the training procedures of this type of SVM. A polynomial kernel or an RBF kernel was set as its kernel.
RF is another powerful classification algorithm, which has been widely applied to tackle various biological problems [48,49,50,51,52,53,54]. In fact, it is an ensemble algorithm, integrating several decision trees. To set up each decision tree, it randomly selects samples from the given dataset, with replacement, and features to extend the tree at each node. For a query sample, all decision trees provide their predictions. These predictions are integrated in RF by majority voting. It is widely accepted that decision tree is a relative weak classifier. However, RF is much more powerful [55].
The above SVM and RF algorithms are all implemented by corresponding tools in Meka [39]. These tools were directly employed in this study.
All multi-label classifiers constructed in this study were assessed by ten-fold cross-validation [56]. Such method first divides the original dataset, denoted by $ D $, into 10 mutually exclusive subsets with similar size, i.e., $ D = {D}_{1}\cup {D}_{2}\cup \dots {D}_{10}, {D}_{i}\cap {D}_{j} = \varnothing (i\ne j, 1\le i, j\le 10) $. Each subset, say $ {D}_{i} $, is picked up as test dataset and remaining nine subsets constitute the training dataset. The classifier built on the training dataset is applied to the test dataset. Thus, each sample is exactly tested once.
For the results of ten-fold cross-validation, we can compute some measurements to assess the quality of results. In this study, we employed three widely used measurements in multi-label classification: accuracy, exact matching and hamming loss. To list their formulas, some notations are necessary. Given a dataset with $ n $ samples and $ m $ labels, suppose that $ {L}_{i} $ and $ {L}_{i}^{'} $ are the sets of true labels and predicted labels, respectively, of the ith sample. Above three measurements can be computed by
$ \left\{Accuracy=1nn∑i=1(‖Li∩L′i‖‖Li∪L′i‖)Exact match=1nn∑i=1∇(Li,L′i)Hamming loss=1nn∑i=1(‖Li∪L′i−Li∩L′i‖m) \right. $
|
(4) |
where $ \nabla $ is defined as below:
$ \nabla \left({L}_{i}, {L}_{i}^{'}\right) = \left\{1if Li is identifical to L′i0otherwise \right. $
|
(5) |
Evidently, the high accuracy and exact match indicate the good performance of the classifier, whereas it is on the contrary for hamming loss.
When comparing the performance of different classifiers, different results may be concluded according to different measurements. The ranges of accuracy, exact match and hamming loss are all between 0 and 1. Accuracy and exact match have the same trend to represent the performance of classifiers, that is, higher value represents higher performance; whereas hamming loss suggest the contrary trend, that is, lower value suggests higher performance. Thus, we refined hamming loss as 1-hamming loss to make it having the same trend as accuracy and exact match. In this case, accuracy, exact match and 1-hamming loss can multiply together to define a new measurement, called integrated score in this study, formulated by
$ Integrated \ score = Accuracy*Exact \ match*\left(1-hamming \ loss\right) $ | (6) |
The higher the integrated score, the higher the performance of the classifier. This measurement has also used in some previous studies [45,57].
In this study, we proposed a multi-label classifier to identify mouse protein functions, incorporating the procedure of analyzing the associations of functional types. Two types of features (domain and network embedding features) were adopted to encode proteins. RAKEL was employed to construct classifiers. The entire procedures are illustrated in Figure 2. In this section, the detailed evaluation results would be given and some comparisons were conducted.
For the protein features derived from its functional domain information, we adopted RAKEL with a certain base classifier to construct multi-label classifiers. Three base classifiers were tried in this study: (1) SVM with polynomial kernel, (2) SVM with RBF kernel, (3) RF. For two types of SVM, the regularization parameter C was set to 0.5, 1 and 2, the exponent of polynomial kernel was set to its default value (one) and the parameter γ of RBF kernel was also set to its default value (0.01). As for RF, its main parameter, number of decision trees, was tuned, including various values between 10 and 300. The main parameter m for RAKEL was set to its default value 10, the other parameter k of RAKEL was wet to 2, 3, 4, 5. The grid search was adopted to set up all RAKEL classifiers, which were assessed by ten-fold cross-validation, and extract the optimum parameters for each base classifier. The best performance, measured by integrated score, for each base classifier is provided in Table 1, in which the best parameters for each base classifier are also provided. The integrated scores for three base classifiers were 0.1026, 0.0611 and 0.1574. Evidently, the RAKEL classifier with RF provided the best performance. Its accuracy, exact match and hamming loss were 0.6025, 0.2806 and 0.0687, respectively, which were all best compared with those of RAKEL classifiers with other two base classifiers. At a glance, these three RAKEL classifiers were not good enough. However, they were better than classifiers without label partition, which would be elaborated in Section 3.4.
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1 | 0.5329 | 0.2087 | 0.0777 | 0.1026 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01 | 0.4643 | 0.1441 | 0.0872 | 0.0611 |
Random forest | m = 10, k = 3, number of decision trees = 250 | 0.6025 | 0.2806 | 0.0687 | 0.1574 |
In addition, to fully evaluate the best RAKEL classifier with a certain base classifier, it was further assessed by ten-fold cross-validation for ten times. The performance under ten-fold cross-validation for ten times is shown in Figure 3, from which we can see that all four measurements yielded by each RAKEL classifier varied in a small range, indicating the classifiers with label partition were quite stable no matter how samples were divided.
For the network embedding features derived from seven protein networks, a similar procedure was conducted. The same parameters were tried for three base classifiers and RAKEL. Furthermore, the dimension of features was also tuned, including 100, 150, 200, 250 and 300. The grid search was also used to build all RAKEL classifiers, which were further assessed by ten-fold cross-validation. The best RAKEL classifier with a certain base classifier was found and its performance is listed in Table 2. The optimum parameters for each base classifier are also provided in this table. The integrated scores for three base classifiers were 0.1308, 0.0714 and 0.1269, respectively. Clearly, the RAKEL classifier with SVM (polynomial kernel) generated the best performance, where the accuracy, exact match and hamming loss were 0.5853, 0.2407 and 0.0713. These measurements were best among those yielded by three RAKEL classifiers listed in Table 2. Compared with the performance of RAKEL classifiers based on domain embedding features, the superiority of RAKEL classifiers with network embedding features depended on the base classifier. The SVM base classifier gave better performance, whereas RF base classifier yielded lower performance.
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, feature dimension = 300 | 0.5853 | 0.2407 | 0.0713 | 0.1308 |
Support vector machine (RBF kernel) |
m = 10, k = 3, C = 2, γ = 0.01, feature dimension=300 | 0.5020 | 0.1551 | 0.0824 | 0.0714 |
Random forest | m = 10, k = 5, number of decision trees = 250, feature dimension = 150 | 0.5727 | 0.2385 | 0.0714 | 0.1269 |
Likewise, for the best RAKEL classifiers with different base classifiers, they were further evaluated by additional ten-fold cross-validation for ten times. A box plot was shown in Figure 4 for each measurement. It is easy to see that each measurement of each classifier was changed in a small range, suggesting the stability of three RAKEL classifiers. This result was almost same as those based on the domain embedding features.
Two types of features were adopted in this study to represent mouse proteins. They indicated essential properties of proteins from different aspects. The combination of these two types of features can be helpful to construct more efficient classifiers. Thus, we constructed RAKEL classifiers using both domain and network embedding features. To save time, we only tried the parameters listed in Tables 1 and 2. The best performance of RAKEL classifiers with different base classifiers are provided in Table 3. The integrated scores for three base classifiers were 0.1619, 0.1096 and 0.1731, respectively. Each of them was higher than the RAKEL classifiers with the same base classifier and domain or network embedding features. Furthermore, it can be observed from Tables 1-3 that given the same base classifier, the classifier with domain and network embedding features always generated higher accuracy, exact match and lower hamming loss than that with only domain or network embedding features. Therefore, the domain and network embedding features can complement each other so that their combination can improve the performance of classifiers.
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.6242 | 0.2777 | 0.0660 | 0.1619 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension = 300 | 0.5439 | 0.2177 | 0.0743 | 0.1096 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.6235 | 0.2963 | 0.0633 | 0.1731 |
In this study, the label partition was employed to construct multi-label classifiers for identifying functions of mouse proteins. To elaborate the merits of label partition, we also built RAKEL classifiers that did not adopt the label partition. All parameters for three base classifiers and RAKEL were tried for each feature type. All such classifiers were also assessed by ten-fold cross-validation.
For the classifiers with each base classifier and domain embedding features, we plotted a violin to show their performance on each measurement under different parameters, as shown in Figure 5. For an easy comparison, those yielded by classifiers that employed the label partition were also provided in this figure. It can be observed that the accuracy, exact match and integrated score yielded by classifiers with label partition were all higher than those obtained by classifiers without label partition. As for the hamming loss, it was on the contrary. All these indicated that the employment of label partition can improve the performance of classifiers. For the other feature type, network embedding features, same tests were conducted. The violins of four measurements are illustrated in Figure 6. The same conclusion can be concluded, that is, the classifiers with label partition were generally superior to those without label partition.
For the classifiers using both domain and network embedding features, we tested them with parameters listed in Table 3 when the label space partition procedure was not used. The results of ten-fold cross-validation are listed in Table 4. Evidently, classifiers without label partition were much inferior to those with label partition, suggesting the effectiveness of the label partition.
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.5059 | 0.1507 | 0.0781 | 0.0703 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension=300 | 0.4485 | 0.1112 | 0.0848 | 0.0456 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.5069 | 0.1608 | 0.0762 | 0.0753 |
The classifiers proposed in this study adopted the label partition yielded by Louvain method. To confirm such obtained partition was really helpful to improve the performance of classifiers, we employed the random label partition, which randomly divided class labels into some partitions. To give a far comparison, the distribution of partition sizes in random partition was same as that in the partition yielded by Louvain method. On each random partition, the best RAKEL classifier with each base classifier and each feature type was built and assessed by ten-fold cross-validation. Such procedures executed ten times for different random partitions. The performance (integrated score) of each RAKEL classifier on two feature types is shown in Figures 7 and 8, respectively. For easy comparisons, the performance of RAKEL classifiers with partition yielded by Louvain method under ten-fold cross-validation for ten times was also listed in these two figures. It can be observed that when the base classifier was SVM (polynomial kernel) or RF, the RAKEL classifiers with partition yielded by Louvain method always generated better performance. As for the base classifier, SVM (RBF kernel), its superiority was not very obvious. It provided relatively better performance using domain embedding features. However, for network embedding features, classifiers with partition yielded by Louvain method were not always better than those with random partition. As a whole, classifiers with partition yielded by Louvain method were superior to those with random partition. The reasonable partition of class labels can further improve the performance of classifiers.
For the classifiers with both domain and network embedding features, we also compared them with those using random partition. The performance of classifier with each base classifier and random partition is listed in Table 5. Compared with results listed in Table 3, classifiers with partition yielded by Louvain method always produced higher accuracy, exact match and integrated score. As for hamming loss, classifiers with random partition yielded lower values when SVM was the base classifier. However, this cannot change the fact that classifiers with partition yielded by Louvain method were superior to the classifiers with random partition.
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.6177 | 0.2705 | 0.0654 | 0.1562 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension=300 | 0.5427 | 0.2138 | 0.0737 | 0.1075 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.6195 | 0.2952 | 0.0635 | 0.1713 |
In references [9,10], two hybrid classifiers were proposed to identify functions of mouse proteins. They contained one network-based classifier, which was constructed based on PPI information reported in STRING. For a query protein, this classifier assigned a score to each of 24 functional types. Then, 24 types were sorted by the decreasing order of corresponding scores. Evidently, this classifier cannot determine which types were the predicted types. To compare with our classifiers, we employed a threshold for such score so that this classifier can determine the predicted types. Various thresholds were tried for this classifier, which was assessed by ten-fold cross-validation for ten times. The highest integrated score was only 0.0160, which was much lower than those listed in Tables 1-3. The accuracy was 0.2532, exact match was 0.0706 and hamming loss was 0.1059. Clearly, such performance was much lower than that of any above-mentioned classifier. This result indicated that the classifiers proposed in this study were superior to this previous classifier.
As mentioned above, the usage of label partition improved the performance of multi-label classifiers. The final classifier should use the label partition on the whole dataset. This section gave analyses on 24 functional types (labels).
First, we constructed a protein subset for each label, which consisted of all proteins having this label. For any two labels, their associations were evaluated by the Tanimoto coefficient of their corresponding protein subsets. A heat map was plotted to show Tanimoto coefficients for any two functional types, as illustrated in Figure 9. It can be observed that class 14 (TRANSPOSABLE ELEMENTS, VIRAL AND PLASMID PROTEINS) has weak associations with almost all other classes. On the contrary, class 7 (PROTEIN WITH BINDING FUNCTION OR COFACTOR REQUIREMENT (structural or catalytic)) and class 21 (SUBCELLULAR LOCALIZATION) were highly related to other classes. By using the Louvain method, 24 functional types were divided into three partitions, which are listed in Table 6. There were 14 functional types in Partition 1, whereas other two partitions all contained five functional types. Not surprisingly, class 7 and class 21 were classified into the same partition. Given a protein representation, a multi-label classifier can be built on each partition. Classifiers on all three partitions were integrated in the final multi-label classifier.
Index | Functional type |
Partition 1 | PROTEIN WITH BINDING FUNCTION OR COFACTOR REQUIREMENT (structural or catalytic) REGULATION OF METABOLISM AND PROTEIN FUNCTION CELLULAR COMMUNICATION/SIGNAL TRANSDUCTION MECHANISM SUBCELLULAR LOCALIZATION CELLULAR TRANSPORT, TRANSPORT FACILITIES AND TRANSPORT ROUTES TRANSCRIPTION ENERGY METABOLISM CELL CYCLE AND DNA PROCESSING PROTEIN FATE (folding, modification, destination) BIOGENESIS OF CELLULAR COMPONENTS SYSTEMIC INTERACTION WITH THE ENVIRONMENT PROTEIN SYNTHESIS CELL RESCUE, DEFENSE AND VIRULENCE |
Partition 2 | INTERACTION WITH THE ENVIRONMENT CELL TYPE LOCALIZATION TISSUE LOCALIZATION ORGAN LOCALIZATION TRANSPOSABLE ELEMENTS, VIRAL AND PLASMID PROTEINS |
Partition 3 | CELL FATE DEVELOPMENT (Systemic) TISSUE DIFFERENTIATION ORGAN DIFFERENTIATION CELL TYPE DIFFERENTIATION |
By employing the association information of functional types, the performance of the multi-label classifiers for identification of mouse protein functions was improved. However, there still exist rooms for improvement. First, protein features are key factors that can influence the performance of classifiers. Some novel and efficient protein features, such as motif embedding features [58], can be adopted to further improve the classifiers. Second, only one community detection algorithm, Louvain method, was employed to cluster functional types in this study. It was not clear whether this algorithm was optimum to deal with this problem. Some novel community detection algorithms may deeply investigate the associations between functional types, thereby producing a more optimum label partition. Finally, we adopted traditional machine learning algorithms (RAKEL, SVM, RF) to construct classifiers. They can be replaced with more powerful algorithms, such as deep learning algorithms, so that more efficient classifiers can be built. In future, we will continue our study in these aspects.
This study proposed a novel multi-label classifier for identification of functions of mouse proteins. Such classifier considered the associations of functional types (labels) and divided labels into some partitions. By employing the label partition, the performance of classifiers was improved. This classifier can be easily extended to other organisms. It is hopeful that this classifier can be helpful to identify novel functions of mouse proteins. All codes and data are available at https://github.com/LiXuuuu/Mouse-Protein.
The authors declare no conflict of interest.
[1] | Costerton JW, Stewart PS, Greenberg EP (1999) Bacterial Biofilms: A Common Cause of Persistent Infections. Science 27: 1318-1322. |
[2] |
Davies D (2003) Understanding biofilm resistance to antibacterial agents. Natur Rev Drug Discov 2: 114-122. doi: 10.1038/nrd1008
![]() |
[3] | Sun G (2014) Prevention of hospital and community acquired infections by using antibacterial textiles and clothing. In: Polymeric materials with antimicrobial activity, Muñoz-Bonilla A, Cerrada ML, Fernández-García M., Eds., RSC Polymer Chemistry Series, Ch 6, 139-154. |
[4] |
Chen L, Bromberg L, Hatton TA, et al. (2008) Electrospun cellulose acetate fibers containing chlorhexidine as a bactericide. Polymer 49: 1266-1275. doi: 10.1016/j.polymer.2008.01.003
![]() |
[5] |
DeLeo FR, Chambers HF (2009) Reemergence of antibiotic-resistant Staphylococcus aureus in the genomics era. J Clin Invest 119: 2464-2474. doi: 10.1172/JCI38226
![]() |
[6] | Melaiye A, Sun Z, Hindi K, et al. (2005) Silver(I)−Imidazole Cyclophane gem-Diol Complexes Encapsulated by Electrospun Tecophilic Nanofibers: Formation of Nanosilver Particles and Antimicrobial Activity. J Am Chem Soc 8: 2285-2291. |
[7] |
Baley GJ, Peck GE, Banker GS (1977) Bactericidal properties of quaternary ammonium compounds in dispersed systems. J Pharm Sci 66: 696-699. doi: 10.1002/jps.2600660522
![]() |
[8] | Ortenzio LF, Stuart LS (1959) The behavior of chlorine-bearing organic compounds in the AOAC available chlorine gemicidal equivalent concentration test. J Assoc Off Ana Chem 42: 630-633. |
[9] |
Chopra I, Roberts M (2001) Tetracycline Antibiotics: Mode of Action, Applications, Molecular Biology, and Epidemiology of Bacterial Resistance. Microbiol Mol Biol Rev 65: 232-260. doi: 10.1128/MMBR.65.2.232-260.2001
![]() |
[10] |
Danese PN (2002) Antibiofilm Approaches: Prevention of Catheter Colonization. Chem Biol 9: 873-880. doi: 10.1016/S1074-5521(02)00192-8
![]() |
[11] |
Jarvis WR (1996) Selected aspects of the socioeconomic impact of nosocomial infections: morbidity, mortality, cost, and prevention. Infect Cont Hosp Ep 17: 552-557. doi: 10.2307/30141291
![]() |
[12] |
McGowan JE (2001) Economic impact of antimicrobial resistance. Emerg Infect Dis 7: 286-292. doi: 10.3201/eid0702.010228
![]() |
[13] | Reneker DH, Chun I (1996) Nanometre diameter fibres of polymer, produced by electrospinning, Nanotechnology 7: 216-223. |
[14] |
Li D, Xia Y (2004) Electrospinning of nanofibers: Reinventing the wheel? Adv Mater 16: 1151-1170. doi: 10.1002/adma.200400719
![]() |
[15] |
Deitzel, JM, Kleinmeyer J, Harris D et al. (2001) The effect of processing variables on the morphology of electrospun nanofibers and textiles. Polymer 42: 261-272. doi: 10.1016/S0032-3861(00)00250-0
![]() |
[16] |
Daels N, De Vrieze S, Sampers I, et al (2011) Potential of a functionalised nanofibre microfiltration membrane as an antibacterial water filter. Desalination 275: 285-290. doi: 10.1016/j.desal.2011.03.012
![]() |
[17] |
Zodrow K, Brunet L, Mahendra S (2009) Polysulfone ultrafiltration membranes impregnated with silver nanoparticles show imporved biofouling resistance and virus removal. Water Res 43: 715-723. doi: 10.1016/j.watres.2008.11.014
![]() |
[18] |
Payne RJH, Jansen VAA (2003) Pharmacokinetic principles of bacteriobacteriophage therapy. Clin Pharmacokinet 42: 315-325. doi: 10.2165/00003088-200342040-00002
![]() |
[19] | Gao Y, Truong YB, Zhu Y, et al. (2014) Electrospun antibacterial nanofibers: production, activity, and in vivo applications. J Appl Polym Sci 131: 40797. |
[20] | Wang S, Zheng F, Huang Y, et al. (2012) Encapsulation of amoxicillin within laponite-doped poly(lactic-co-glycolic acid) nanofibers: preparation, characterization, and antibacterial activity. Acs Appl Mater Interfaces 4: 6396-6401. |
[21] |
Özdemir I, Özcan EO, Günal S (2010) Synthesis and Antimicrobial Activity of Novel Ag-N-Heterocyclic Carbene Complexes. Molecules 15: 2499-2508. doi: 10.3390/molecules15042499
![]() |
[22] |
Clement JL, Jarret PS (1994) Antibacterial Silver. Met Based Drugs 1: 467-482. doi: 10.1155/MBD.1994.467
![]() |
[23] |
Tambe SM, Sampath L, Modak SM (2001) In-vitro evaluation of the risk of developing bacterial resistance to antiseptics and antibiotics used in medical devices. J Antimicrob Chemoth 47: 589-598. doi: 10.1093/jac/47.5.589
![]() |
[24] |
Liu JJ, Galettis P, Farr A, et al. (2008) In vitro antitumour and hepatotoxicity profiles of Au(I) and Ag(I) bidentate pyridyl phosphine complexes and relationships to cellular uptake. J Inorg Biochem 102: 303-310. doi: 10.1016/j.jinorgbio.2007.09.003
![]() |
[25] | Jeong L, Kim MH, Jung JY, et al. (2014) Effect of silk fibroin nanofibers containing silver sulfadiazine on wound healing. Int J Nanomed 9: 5277-5287. |
[26] |
Burd A, Kwok CH, Hung SC, et al. (2007) A comparative study of the cyto¬toxicity of silver-based dressings in monolayer cell, tissue explant, and animal model. Wound Repair Regen 15: 94-104. doi: 10.1111/j.1524-475X.2006.00190.x
![]() |
[27] |
Lok CN, Ho CM, Chen R, et al. (2006) Proteomic analysis of the mode of antibacterial action of silver nanoparticles. J Proteome Res 5: 916-924. doi: 10.1021/pr0504079
![]() |
[28] | Lansdown AB (2005) A guide to the properties and uses of silver dressings in wound care. Prof Nurse 20: 41-43. |
[29] |
Vepari C, Kaplan DL (2007) Silk as a biomaterial. Prog Polym Sci 32: 991-1007. doi: 10.1016/j.progpolymsci.2007.05.013
![]() |
[30] |
Kasoju N, Bora U (2012) Silk fibroin in tissue engineering. Adv Healthc Mater 1: 393-412. doi: 10.1002/adhm.201200097
![]() |
[31] |
Dong G, Xiao X, Liu X, et al. (2009) Functional Ag porous films prepared by electrospinning. J Appl Surf Sci 255: 7623-7626. doi: 10.1016/j.apsusc.2009.04.039
![]() |
[32] |
Xu X, Yang Q, Wang Y, el al. (2006) Biodegradable electrospun poly(l-lactide) fibers containing antibacterial silver nanoparticles. Eur Polym J 42: 2081-2087. doi: 10.1016/j.eurpolymj.2006.03.032
![]() |
[33] |
Son WK, Youk JH, Park WH (2006) Antimicrobial cellulose acetate nanofibers containing silver nanoparticles. Carbohyd Polym 65: 430-434. doi: 10.1016/j.carbpol.2006.01.037
![]() |
[34] |
Lala NL, Ramaseshan R, Bojun L, et al. (2007) Fabrication of nanofibers with antimicrobial functionality used as filters: protection against bacterial contaminants. Biotechnol Bioeng 97: 1357-1365. doi: 10.1002/bit.21351
![]() |
[35] | Yang QB, Li DM, Hong YL, el al. (2003) Preparation and characterization of a PAN nanofibre containing Ag nanoparticles via electrospinning Proceedings of the 2002 International Conference on Science and Technology of Synthetic Metals, Elsevier SA, Lausanne, Switzerland, 973-974. |
[36] |
Sheikh FA, Barakat NAM, Kanjwal MA, el al. (2009) Electrospun antimicrobial polyurethane nanofibers containing silver nanoparticles for biotechnological applications. Macromol Res 17: 688-696. doi: 10.1007/BF03218929
![]() |
[37] | Gliscinska E, Gutarowska B, Brycki B, el al. (2012) Electrospun Polyacrylonitrile Nanofibers Modified by Quaternary Ammonium Salts. J Appl Polym Sci 128: 767-775. |
[38] |
Yarin AL, Zussman E (2004) Upward needleless electrospinning of multiple nanofibers. Polymer 45: 2977-2980. doi: 10.1016/j.polymer.2004.02.066
![]() |
[39] |
Nomiya K, Tsuda K, Sudoh T, el al. (1997) Ag(I)-N bond-containing compound showing wide spectra in effective antimicrobial activities: Polymeric silver(I) imidazolate. J Inorg Biochem 68: 39-44. doi: 10.1016/S0162-0134(97)00006-8
![]() |
[40] | Nomiya K, Noguchi R, Oda M (2000) Synthesis and crystal structure of coinage metal(I) complexes with tetrazole (Htetz) and triphenylphosphine ligands, and their antimicrobial activities. A helical polymer of silver(I) complex [Ag(tetz)(PPh3)2]n and a monomeric gold(I) complex [Au(tetz)(PPh3)]. Inorg Chim Acta 298: 24-32. |
[41] |
Atiyeh BS, Ioannovich J, Al-Amm CA, el al. (2002) Management of acute and chronic open wounds: the importance of moist environment in opimal wound healing. Curr Pharm Biotechnol 3: 179-195. doi: 10.2174/1389201023378283
![]() |
[42] |
Akhavan O, Ghaderi E (2009) Enhancement of antibacterial properties of Ag nanorods by electric field. Sci Technol Adv Mat 10: 015003. doi: 10.1088/1468-6996/10/1/015003
![]() |
[43] |
Tijing LD, Amarjargal A, Jiang Z (2013) Antibacterial tourmaline nanoparticles/polyurethane hybrid mat decorated with silver nanoparticles prepared by electrospinning and UV photoreduction. Curr Appl Phys 13: 205-210. doi: 10.1016/j.cap.2012.07.011
![]() |
[44] |
Raffi M, Mehrwan S, Bhatti TM, et al. (2010) Investigations into the antibacterial behavior of copper nanoparticles against Escherichia coli. Ann Microbiol 60: 75-80. doi: 10.1007/s13213-010-0015-6
![]() |
[45] |
Zhong W, Zishena W, Zhenhuana Y, et al. (1994) Synthesis, characterization and antifungal activity of copper (II), zinc (II), cobalt (II) and nickel (II) complexes derived from 2-chlorobenzaldehyde and glycine. Synth React Inorg Met-Org Chem 24: 1453-1460. doi: 10.1080/00945719408002572
![]() |
[46] |
Sheikh FA, Kanjwal MA, Saran S, et al. (2011) Polyurethane nanofibers containing copper nanoparticles as future materials. Appl Surf Sci 257: 3020-3026. doi: 10.1016/j.apsusc.2010.10.110
![]() |
[47] |
Hwang SH, Song J, Jung Y, et al. (2011) Electrospun ZnO/TiO2 composite nanofibers as a bactericidal agent. J Chem Commun 47: 9164-9166. doi: 10.1039/c1cc12872h
![]() |
[48] |
Lee S (2009) Multifunctionality of layered fabric systems based on electrospun polyurethane/zinc oxide nanocomposite fibers. J Appl Polym Sci 114: 3652-3658. doi: 10.1002/app.30778
![]() |
[49] |
Lee K, Lee S (2012) Multifunctionality of poly(vinyl alcohol) nanofiber webs containing titanium dioxide. J Appl Polym Sci 124: 4038-4046. doi: 10.1002/app.34929
![]() |
[50] | Yu W, Lan CH, Wang SJ, et al. (2010), Influence of Zinc oxide nanoparticles on the crystallization behavior of electrospun poly(3-hydroxybutyrate-co-3-hydroxyvalerate) nanofibers. Polymer 51: 2403. |
[51] |
Drew C, Liu X, Ziegler D, et al. (2003) Metal oxide-coated polymer nanofibers. Nano Lett 3: 143-147. doi: 10.1021/nl025850m
![]() |
[52] |
Horzum N, Mari M, Wagner M, et al. (2015) Controlled surface mineralization of metal oxides on nanofibers. RSC Adv 5: 37340-37345. doi: 10.1039/C5RA02140E
![]() |
[53] |
Klabunde KJ, Stark J, Koper O, et al. (1996) Nanocrystals as stoichiometric reagents with unique surface chemistry. J Phys Chem 100: 12142-12153. doi: 10.1021/jp960224x
![]() |
[54] |
Stoimenov PK, Klinger RL, Marchin GL (2002) Metal Oxide Nanoparticles as Bactericidal Agents. Langmuir 18: 6679-6686. doi: 10.1021/la0202374
![]() |
[55] | Koper O, Klabunde J, Marchin G, et al. (2012) Nanoscale Powders and Formulations with Biocidal Activity Toward Spores and Vegetative Cells of Bacillus Species, Viruses, and Toxins. Curr Microbiol 44: 49-55. |
[56] | McDonnell G, Russell AD (1999) Antiseptics and disinfectants: activity, action, and resistance. Clin Microbiol Rev 12: 147-179. |
[57] | Kügler R, Bouloussa O, Rondelez F (2005) Evidence of a charge-density threshold for optimum Synthesis and antimicrobial activity of modified poly(glycidyl methacrylate-co-2-hydroxy -ethyl methacrylate) derivatives with quaternary ammonium and phosphonium salts. J Polym Sci Pol Chem 40: 2384-2393. |
[58] | Kenawy ER, Abdel-Hay FI, El-Shanshoury A, et al. (2002) Biologically active polymers. V. Synthesis and antimicrobial activity of modified poly(glycidyl methacrylate-co-2-hydroxyethyl methacrylate) derivatives with quaternary ammonium and phosphonium salts. J Polym Sci Part A: Polym Chem 40: 2384-2393. |
[59] |
Kenawy ER, Mahmoud YAG (2003) Biological active polymers. Macromol Biosci 3: 107-116. doi: 10.1002/mabi.200390016
![]() |
[60] |
Lundin JG, Coneski PN, Fulmer PA, et al. (2014) Relationship between surface concentration of amphiphilic quaternary ammonium biocides in electrospun polymer fibers and biocidal activity. React Funct Polym 77: 39-46. doi: 10.1016/j.reactfunctpolym.2014.02.004
![]() |
[61] |
Arumugam GK, Khan S, Heiden PA (2009) Comparison of the Effects of an Ionic Liquid and Other Salts on the Properties of Electrospun Fibers, 2-Poly(vinyl alcohol). Macromol Mater Eng 294: 45-53. doi: 10.1002/mame.200800199
![]() |
[62] |
You Y, Lee SJ, Min BM, et al. (2006) Effect of solution properties on nanofibrous structure of electrospun poly(lactic-co-glycolic acid). J Appl Polym Sci 99: 1214-1221. doi: 10.1002/app.22602
![]() |
[63] |
Tong HW, Wang M (2011) Electrospinning of poly(hydroxybutyrate-co-hydroxyvalerate) fibrous scaffolds for tissue engineering applications: effects of electrospinning parameters and solution properties. J Macromol Sci 50: 1535-1558. doi: 10.1080/00222348.2010.541008
![]() |
[64] |
Park JA, Kim SB (2015) Preparation and characterization of antimicrobial electrospun poly(vinyl alcohol) nanofibers containing benzyl triethylammonium chloride. React Funct Polym 93: 30-37. doi: 10.1016/j.reactfunctpolym.2015.05.008
![]() |
[65] |
Kim SJ, Nam YS, Rhee DM, et al. (2007) Preparation and characterization of antimicrobial polycarbonate nanofibrous membrane. Eur Polym J 43: 3146-3152. doi: 10.1016/j.eurpolymj.2007.04.046
![]() |
[66] |
Nicosia A, Gieparda W, Foksowicz-Flaczyk J, et al. (2015) Air filtration and antimicrobial capabilities of electrospun PLA/PHB containing ionic liquid. Sep Purif Technol 154: 154-160. doi: 10.1016/j.seppur.2015.09.037
![]() |
[67] |
Buruiana EC, Buruiana T (2002) Recent developments in polyurethane cationomers. Photoisomerization reactions in azoaromatic polycations. J Photoch Photobiol A: Chem 151: 237-252. doi: 10.1016/S1010-6030(02)00181-8
![]() |
[68] |
Uykun N, Ergal I, Kurt H, et al. (2014) Electrospun antibacterial nanofibrous polyvinylpyrrolidone/ cetyltrimethylammonium bromide membranes for biomedical applications. J Bioact Compat Pol 29: 382-397. doi: 10.1177/0883911514535153
![]() |
[69] |
Qun XL, Fanf Y, Shan Y, et al. (2010) Antibacterial Nanofibers of Self-quaternized Block Copolymers of 4-Vinyl Pyridine and Pentachlorophenyl Acrylate. High Perform Polym 22: 359-376. doi: 10.1177/0954008309104776
![]() |
[70] |
Cashion MP, Li X, Geng Y, at al. (2010) Gemini Surfactant Electrospun Membranes. Langmuir 26: 678-683. doi: 10.1021/la902287b
![]() |
[71] |
Singh G, Bittner AM, Loscher S, et al. (2008) Electrospinning of diphenylalanine nanotube. Adv Mater 20: 2332-2336. doi: 10.1002/adma.200702802
![]() |
[72] |
Worley SD, Williams DE (1988) Halamine Water Disinfectants. Crit Rev Environ Control 18: 133-175. doi: 10.1080/10643388809388345
![]() |
[73] |
Song J, Jang J (2014) Antimicrobial polymer nanostructures: Synthetic route, mechanism of action and perspective. Adv Colloid Interface Sci 203: 37-50. doi: 10.1016/j.cis.2013.11.007
![]() |
[74] |
Cerkez I, Worley SD, Broughton RM, et al. Huang (2013) Rechargeable antimicrobial coatings for poly(lactic acid) nonwoven fabrics. Polymer 54: 536-541. doi: 10.1016/j.polymer.2012.11.049
![]() |
[75] |
Chen Z, Sun Y (2005) N-Chloro-Hindered Amines as Multifunctional Polymer Additives. Macromolecules 38: 8116-8119. doi: 10.1021/ma050874b
![]() |
[76] | Chen Z, Sun Y (2005) Antimicrobial Functions of N-Chloro-Hindered Amines. Polym Preprint 46: 835-836. |
[77] |
Dong A, Zhang Q, Wang T, et al. (2010) Immobilization of cyclic N-halamine on polystyrene-functionalized silica nanoparticles: synthesis, characterization, and biocidal activity. J Phys Chem C 114: 17298-17303. doi: 10.1021/jp104083h
![]() |
[78] |
Kang J, Han J, Gao Y, et al. (2015) Unexpected Enhancement in Antibacterial Activity of N-Halamine Polymers from Spheres to Fibers. ACS Appl Mater Interfaces 7: 17516-17526. doi: 10.1021/acsami.5b05429
![]() |
[79] |
Sun X, Zhang L, Cao Z, et al. (2010) Electrospun Composite Nanofiber Fabrics Containing Uniformly Dispersed Antimicrobial Agents As an Innovative Type of Polymeric Materials with Superior Antimicrobial Efficacy. ACS Appl Mater Interfaces 2: 952-956. doi: 10.1021/am100018k
![]() |
[80] |
Tan K, Obendorf SK (2007) Fabrication and evaluation of electrospun antimicrobial nylon 6 membranes. J Membrane Sci 305: 287-298. doi: 10.1016/j.memsci.2007.08.015
![]() |
[81] | Dickerson MB, Sierra AA, Bedford NM, et al. (2013) Keratin-based antimicrobial textiles, films, and nanofibers. J Mater Chem B 1: 5505-5514. |
[82] |
Fan X, Jiang Q, Sun Z, et al. (2015) Preparation and Characterization of Electrospun Antimicrobial Fibrous Membranes Based on Polyhydroxybutyrate (PHB). Fiber Polym 16: 1751-1758. doi: 10.1007/s12221-015-5108-1
![]() |
[83] |
Ignatova M, Rashkov I, Manolova N (2013) Drug-loaded electrospun materials in wound-dressing applications and in local cancer treatment. Expert Opin Drug Del 10: 469-483. doi: 10.1517/17425247.2013.758103
![]() |
[84] |
Kim K, Luu YK, Chang C et al. (2004) Incorporation and controlled release of a hydrophilic antibiotic using poly(lactide-co-glycolide)-based electrospun nanofibrous scaffolds. J Control Release 98: 47-56. doi: 10.1016/j.jconrel.2004.04.009
![]() |
[85] |
Yoo HS, Kim TG, Park TG (2009) Surface-functionalized electrospun nanofibers for tissue engineering and drug delivery. Adv Drug Deliver Rev 61: 1033-1042. doi: 10.1016/j.addr.2009.07.007
![]() |
[86] |
Zong XH, Li S, Chen E, at al. (2004) Prevention of postsurgery-induced abdominal adhesions by electrospun bioabsorbable nanofibrous poly(lactide-co-glycolide)-based membranes. Ann Surg 240: 910-915. doi: 10.1097/01.sla.0000143302.48223.7e
![]() |
[87] |
Hong Y, Fujimoto K, Hashizume R, et al. (2008) Generating elastic, biodegradable polyurethane/poly(lactide-co-glycolide) fibrous sheets with controlled antibiotic release via two-stream electrospinning. Biomacromolecules 9: 1200-1207. doi: 10.1021/bm701201w
![]() |
[88] |
Thakur RA, Florek CA, Kohn J, et al. (2008) Electrospun nanofibrous polymeric scaffold with targeted drug release profiles for potential application as wound dressing. Int J Pharm 364: 87-93. doi: 10.1016/j.ijpharm.2008.07.033
![]() |
[89] |
Gilchrist SE, Lange D, Letchford DK, et al. (2013) Fusidic acid and rifampicin co-loaded PLGA nanofibers for the prevention of orthopedic implant associated infections. J Control Release 170: 64-73. doi: 10.1016/j.jconrel.2013.04.012
![]() |
[90] |
Qi R, Guo R, Shen M, et al. (2010) Electrospun poly(lactic-co-glycolic acid)/halloysite nanotube composite nanofibers for drug encapsulation and sustained release. J Mater Chem 20: 10622-10629. doi: 10.1039/c0jm01328e
![]() |
[91] |
Moghe AK, Gupta BS (2008) Co‐axial Electrospinning for Nanofiber Structures: Preparation and Applications. Polym Rev 48: 353-377. doi: 10.1080/15583720802022257
![]() |
[92] |
Viseras C, Cerezo P, Sanchez R, et al. (2010) Current challenges in clay minerals for drug delivery. Appl Clay Sci 48: 291-295. doi: 10.1016/j.clay.2010.01.007
![]() |
[93] |
McMurry LM, Oethinger M, Levy SB (1998) Triclosan targets lipid synthesis. Nature 394: 531-532. doi: 10.1038/28970
![]() |
[94] | Green JBD, Fulghum T, Nordhaus MA (2011, Immobilized antimicrobial agents: a critical perspective, In: Science Against Microbial Pathogens: Communicating Current Research and Technological Advances, Formatex Microbiology Books Series, Méndez-Vila E, Ed., Badajoz (Spain). |
[95] |
Kaehn K (2010) Polihexanide: A Safe and Highly Effective Biocide. Skin Pharmacol Physiol 23: 7-16. doi: 10.1159/000318237
![]() |
[96] |
Gilbert P, Pemberton D, Wilkinson DE (1990) Synergism within polyhexamethylene biguanide biocide formulations. J Appl Bacteriol 69: 593-598. doi: 10.1111/j.1365-2672.1990.tb01553.x
![]() |
[97] |
del Valle LJ, Camps R, Díaz A, et al. (2011) Electrospinning of polylactide and polycaprolactone mixtures for preparation of materials with tunable drug release properties. J Polym Res 18: 1903-1917. doi: 10.1007/s10965-011-9597-3
![]() |
[98] |
del Valle LJ, Díaz A, Royo M, et al. (2012) Biodegradable polyesters reinforced with triclosan loaded polylactide micro/nanofibers: Properties, release and biocompatibility. Express Polym Lett 6: 266-282. doi: 10.3144/expresspolymlett.2012.30
![]() |
[99] |
Llorens E, Bellmunt S, del Valle LJ, et al. (2014) Scaffolds constituted by mixed polylactide and poly(ethylene glycol) electrospun microfibers. J Polym Res 21: 603. doi: 10.1007/s10965-014-0603-4
![]() |
[100] |
Llorens E, Ibañez H, del Valle LJ, et al. (2015) Biocompatibility and drug release behavior of scaffolds prepared by coaxial electrospinning of poly(butylene succinate) and polyethylene glycol. Mater Sci Eng C 49: 472-484. doi: 10.1016/j.msec.2015.01.039
![]() |
[101] |
Llorens E, del Valle LJ, Puiggalí J (2015) Electrospun scaffolds of polylactide with a different enantiomeric content and loaded with anti-inflammatory and antibacterial drugs. Macromol Res 23: 636-648. doi: 10.1007/s13233-015-3082-5
![]() |
[102] |
Veiga M, Merino M, Cirri M. et al. (2005) Comparative study on triclosan interactions in solution and in the solid state with natural and chemically modified cyclodextrins. J Incl Phenom Macrocycl Chem 53: 77-83. doi: 10.1007/s10847-005-1047-6
![]() |
[103] |
Kayaci F, Umu OCO, Tekinay T, et al. (2013) Antibacterial electrospun Poly(lactic acid) (PLA) nanofibrous webs incorporating triclosan/cyclodextrin inclusion Complexes. J Agric Food Chem 61: 3901-3908. doi: 10.1021/jf400440b
![]() |
[104] |
Celebioglu A, Umu OCO, Tekinay T, et al. (2014) Antibacterial electrospun nanofibers from triclosan/cyclodextrin inclusion complexes. Colloid Surface B 116: 612-619. doi: 10.1016/j.colsurfb.2013.10.029
![]() |
[105] |
Chen L, Bromberg L, Hatton TA (2008) Electrospun cellulose acetate fibers containing chlorhexidine as a bactericide. Polymer 49: 1266-1275. doi: 10.1016/j.polymer.2008.01.003
![]() |
[106] |
Ignatova M, Stoilova O, N Manolova N, et al. (2010) Electrospun mats from styrene/maleic anhydride copolymers: Modification with amines and assessment of antimicrobial activity. Macromol Biosci 10: 944-954. doi: 10.1002/mabi.200900433
![]() |
[107] |
Fernandes JG, Correia DM, Botelho G, et al. (2014) PHB-PEO electrospun fiber membranes containing chlorhexidine for drug delivery applications. Polym Test 34: 64-71. doi: 10.1016/j.polymertesting.2013.12.007
![]() |
[108] | del Valle LJ, Roa M, Díaz A, et al. (2012) Electrospun nanofibers of a degradable poly(ester amide). Scaffolds loaded with antimicrobial agents. J Polym Res 19: 9792. |
[109] |
Díaz A, Katsarava R, Puiggalí J (2014) Synthesis, properties and applications of biodegradable polymers derived from diols and dicarboxylic Acids: From Polyesters to poly(ester amide)s. Int J Mol Sci 15: 7064-7123. doi: 10.3390/ijms15057064
![]() |
[110] | Rodríguez-Galán A, Franco L, Puiggalí J (2011) Degradable poly(ester amide)s for biomedical applications. Polymers 3: 65-99. |
[111] |
Murase SK, del Valle LJ, Kobauri S, et al. (2015) Electrospun fibrous mats from a L-phenylalanine based poly(ester amide): Drug delivery and accelerated degradation by loading enzymes. Polym Degrad Stab 119: 275-287. doi: 10.1016/j.polymdegradstab.2015.05.018
![]() |
[112] |
Díaz A, del Valle LJ, Tugushi D, et al. (2015) New poly(ester urea) derived from L-leucine: Electrospun scaffolds loaded with antibacterial drugs and enzymes. Mater Sci Eng C 46: 450-462. doi: 10.1016/j.msec.2014.10.055
![]() |
[113] | Saha K, Butola BS, Joshi M (2014) Drug-loaded polyurethane/clay nanocomposite nanofibers for topical drug-delivery application. J Appl Polym Sci 131: 40230. |
[114] |
Llorens E, Calderón S, del Valle LJ, et al. (2015) Polybiguanide (PHMB) loaded in PLA scaffolds displaying high hydrophobic, biocompatibility and antibacterial properties. Mater Sci Eng C 50: 74-84. doi: 10.1016/j.msec.2015.01.100
![]() |
[115] | Dash M, Chiellini F, Ottenbrite RM, et al. (2007) Chitosan-a versatile semi-synthetic polymer in biomedical applications. Prog Polym Sci 36: 981-1014. |
[116] | Fischer TH, Bode AP, Demcheva M, et al. (2007) Hemostatic properties of glucosamine-based materials. J Biomed Mater Res A 80: 167-174. |
[117] | Muzzarelli RAA, Belcher R, Freisers H, In: Chitosan in natural chelating polymer; alginic acid, chitin and chitosan, Pergamon Press, Oxford, Pergamon Press, Oxford, 1973, 144-176. |
[118] |
Geng X, Kwon OH, Jang J (2005) Electrospinning of chitosan dissolved in concentrated acetic acid solution. Biomaterials 26: 5427-5432. doi: 10.1016/j.biomaterials.2005.01.066
![]() |
[119] | Spasova M, Manolova N, Paneva D, et al (2004) Preparation of chitosan-containing nanofibres by electrospinning of chitosan/poly(ethylene oxide) blend solutions. e-Polymers 4: 624-635. |
[120] |
Bhattaraia B, Edmondson D, Veiseh O, et al. (2005) Electrospun chitosan-based nanofibers and their cellular compatibility. Biomaterials 26: 6176-6184. doi: 10.1016/j.biomaterials.2005.03.027
![]() |
[121] |
Ignatova M, Starbova K, Markova N, et al. (2006) Electrospun nano-fibre mats with antibacterial properties from quaternised chitosan and poly(vinyl alcohol). Carbohyd Res 341: 2098-2107. doi: 10.1016/j.carres.2006.05.006
![]() |
[122] |
Min BM, Lee SW, Lim JN, et al. (2004) Chitin and chitosan nanofibers: Electrospinning of chitin and deacetylation of chitin nanofibers. Polymer 45: 7137-7142. doi: 10.1016/j.polymer.2004.08.048
![]() |
[123] |
Ohkawa K, Cha D, Kim H, et al. (2004) Electrospinning of chitosan. Macromol Rapid Comm 25: 1600-1605. doi: 10.1002/marc.200400253
![]() |
[124] |
Neamnark A, Rujiravaniti R, Supaphol P (2006) Electrospinning of hexanoyl chitosan. Carbohydrate. Polymers 66: 298-305. doi: 10.1016/j.carbpol.2006.03.015
![]() |
[125] |
Homayoni H, Ravandi SH, Valizadeh M (2009) Electrospinning of chitosan nanofibers: Processing optimization. Carbohyd Polym 77: 656-661. doi: 10.1016/j.carbpol.2009.02.008
![]() |
[126] |
Zong X, Kim K, Fang D, et al. (2002) Structure and process relationship on electrospun bioabsorbable nanofiber membranes. Polymer 43: 4403-4412. doi: 10.1016/S0032-3861(02)00275-6
![]() |
[127] |
Au HT, Pham LN, Vu THT, et al. (2012) Fabrication of an antibacterial non-woven mat of a poly(lactic acid)/chitosan blend by electrospinning. Macromol Res 20: 51-58. doi: 10.1007/s13233-012-0010-9
![]() |
[128] |
Shalumon KT, Anulekha KH, Girish CM, et al. (2010) Single step electrospinning of chitosan/poly(caprolactone) nanofibers using formic acid/acetone solvent mixture. Carbohyd Polym 80: 413-417. doi: 10.1016/j.carbpol.2009.11.039
![]() |
[129] |
Cooper A, Oldinski R, Ma H, et al. (2013) Chitosan-based nanofibrous membranes for antibacterial filter applications. Carbohyd Polym 92: 254-259. doi: 10.1016/j.carbpol.2012.08.114
![]() |
[130] |
Zheng H, Du Y, Yu J, et al. (2001) Preparation and characterization of chitosan/poly(vinyl alcohol) blend fibers. J Appl Polym Sci 80: 2558-2565. doi: 10.1002/app.1365
![]() |
[131] |
Chuang WY, Young TH, Yao CH, et al. (1999) Properties of the poly(vinyl alcohol)/chitosan blend and its effect on the culture of fibroblast in vitro. Biomaterials 20: 1479-1487. doi: 10.1016/S0142-9612(99)00054-X
![]() |
[132] |
Jia YT, Gong J, Gu XH, et al. (2007) Fabrication and characterization of poly(vinyl alcohol)/chitosan blend nanofibers produced by electrospinning method. Carbohyd Polym 67: 403-409. doi: 10.1016/j.carbpol.2006.06.010
![]() |
[133] |
Zhang H, Li S, White CJB, et al. (2009) Studies on electrospun nylon-6/chitosan complex nanofiber interactions. Electrochim Acta 54: 5739-5745. doi: 10.1016/j.electacta.2009.05.021
![]() |
[134] |
Lin SJ, Hsiao WU, Jee SH, et al. (2006) Study on the effects of nylon–chitosan-blended membranes on the spheroid-forming activity of human melanocytes. Biomaterials 27: 5079-5088. doi: 10.1016/j.biomaterials.2006.05.035
![]() |
[135] |
Ma Z, Kotaki M, Yong T, et al. (2005) Surface engineering of electrospun polyethylene terephthalate (PET) nanofibers towards development of a new material for blood vessel engineering. Biomaterials 26: 2527-2536. doi: 10.1016/j.biomaterials.2004.07.026
![]() |
[136] |
Jung KH, Huh MW, Meng W, et al. (2007) Preparation and antibacterial activity of PET/chitosan nanofibrous mats using an electrospinning technique. J Appl Polym Sci 105: 2816-2823. doi: 10.1002/app.25594
![]() |
[137] |
An J, Zhang H, Zhang JT, et al. (2009) Preparation and antibacterial activity of electrospun chitosan/poly(ethylene oxide) membranes containing silver nanoparticles. Colloid Polym Sci 287: 1425-1434. doi: 10.1007/s00396-009-2108-y
![]() |
[138] |
Zhuang XP, Cheng BW, Kang WM, et al. (2010) Electrospun chitosan/gelatin nanofibers containing silver nanoparticles. Carbohyd Polym 82: 524-527. doi: 10.1016/j.carbpol.2010.04.085
![]() |
[139] |
Cai ZX, Mo XM, Zhang KH, et al. (2010) Fabrication of chitosan/silk fibroin composite nanofibers for wound dressing applications. Int J Mol Sci 11: 3529-3539. doi: 10.3390/ijms11093529
![]() |
[140] |
Alipour SM, Nouri M, Mokhtari J, et al. (2009) Electrospinning of poly(vinyl alcohol)–water-soluble quaternized chitosan derivative blend. Carbohyd Res 344: 2496-2501. doi: 10.1016/j.carres.2009.10.004
![]() |
[141] |
Fu GD, Yao F, Li Z, et al. (2008) Solvent-resistant antibacterial microfibers of self-quaternized block copolymers from atom transfer radical polymerization and electrospinning. J Mater Chem 18: 859-867. doi: 10.1039/b716127a
![]() |
[142] |
Parfitt T (2005) Georgia: an unlikely stronghold for bacteriophage therapy. The Lancet 365: 2166-2167. doi: 10.1016/S0140-6736(05)66759-1
![]() |
[143] | Sulakvelidze A, Kutter E (2005) Bacteriophage therapy in humans, In: Bacteriophages: Biology and Applications, Kutter E, Sulakvelidze A, Eds., CRC Press, Boca Raton, FL, 381-436. |
[144] |
Kutter E, De Vos D, Gvasalia G, et al. (2010) Phage therapy in clinical practice: treatment of human infections. Curr Pharm Biotechnol 11: 69-86. doi: 10.2174/138920110790725401
![]() |
[145] |
Abedon S, Kuhl S, Blasdel B, et al. (2011) Phage treatment of human infections. Bacteriophage 1: 66-85. doi: 10.4161/bact.1.2.15845
![]() |
[146] | FDA (2006) FDA approval of Listeria-specific bacteriophage preparation on ready-to-eat (RTE) meat and poultry products. FDA, Washington, DC. |
[147] |
Frykberg RG, Armstrong DG, Giurini J, et al. (2000) Diabetic foot disorders: a clinical practice guideline. J Foot Ankle Surg 39: S1-S60. doi: 10.1016/S1067-2516(00)80057-5
![]() |
[148] |
Anany H, Chen W, Pelton R (2011) Biocontrol of Listeria monocytogenes and Escherichia coli O157:H7 in Meat by Using Phages Immobilized on Modified Cellulose Membranes. Appl Environ Microb 77: 6379-6387. doi: 10.1128/AEM.05493-11
![]() |
[149] |
Markoishvili K, Tsitlanadze G, Katsarava R, et al. (2002) A novel sustained-release matrix based on biodegradable poly(ester amide)s and impregnated with bacteriophages and an antibiotic shows promise in management of infected venous stasis ulcers and other poorly healing wounds. Int J Dermatol 41: 453-458. doi: 10.1046/j.1365-4362.2002.01451.x
![]() |
[150] | Katsarava R, Alavidze Z (2004) Polymer Blends as Biodegradable Matrices for Preparing Biocomposites, US Patent 6,703,040 (Assigned to Intralytix, Inc.). |
[151] | Katsarava R, Gomurashvili Z (2011) Biodegradable Polymers Composed of Naturally Occurring α-Amino Acids, In: Handbook of Biodegradable Polymers—Isolation, Synthesis, Characterization and Applications. Lendlein A, Sisson A, Eds., Wiley-VCH, Verlag GmbH & Co KGaA, Ch. 5. |
[152] |
Jikia D, Chkhaidze N, Imedashvili E, et al. (2005) The use of a novel biodegradable preparation capable of the sustained release of bacteriophages and ciprofloxacin, in the complex treatment of multidrug - resistant Staphylococcus aureus-infected local radiation injuries caused by exposure to Sr90. Clin Exp Dermatol 30: 23-26. doi: 10.1111/j.1365-2230.2004.01600.x
![]() |
[153] |
Puapermpoonsiri U, Spencer J, van der Walle CF (2009) A freeze-dried formulation of bacteriophage encapsulated in biodegradable microspheres. Eur J Pharm Biopharm 72: 26-33. doi: 10.1016/j.ejpb.2008.12.001
![]() |
[154] |
Gervais L, Gel M, Allain B, et al. (2007) Immobilization of biotinylated bacteriophages on biosensor surfaces. Sensor Actuat B-Chem 125: 615-621. doi: 10.1016/j.snb.2007.03.007
![]() |
[155] |
Nanduri V, Balasubramanian S, Sista S, et al. (2007) Highly sensitive phage-based biosensor for the detection of β-galactosidase. Anal Chim Acta 589: 166-172. doi: 10.1016/j.aca.2007.02.071
![]() |
[156] | Tawil N, Sacher E, Rioux D, et al. (2015) Surface Chemistry of Bacteriophage and Laser Ablated Nanoparticle Complexes for Pathogen Detection. J Phys Chem C 119: 14375-14382. |
[157] | Sun W, Brovko L, Griffiths M (2001) Food-borne pathogens. Use of bioluminescent salmonella for assessing the efficiency of Constructed phage-based biosorbent. J Ind Microbiol Biot 27: 126-128. |
[158] |
Cademartiri R, Anany H, Gross I, et al. (2010). Immobilization of bacteriophages on modified silica particles. Biomaterials 31: 1904-1910. doi: 10.1016/j.biomaterials.2009.11.029
![]() |
[159] |
Bennett AR, Davids FGC, Vlahodimou S, et al. (1997) The use of bacteriophage-based systems for the separation and concentration of Salmonella. J Appl Microbiol 83: 259-265. doi: 10.1046/j.1365-2672.1997.00257.x
![]() |
[160] |
Pearson HA, Sahukhal GS, Elasri MO, et al. (2013) Phage-Bacterium War on Polymeric Surfaces: Can Surface-Anchored Bacteriophages Eliminate Microbial Infections? Biomacromolecules 14: 1257-1261. doi: 10.1021/bm400290u
![]() |
[161] |
Dai M, Senecal A, Nugen SR (2014) Electrospun water-soluble polymer nanofibers for the dehydration and storage of sensitive reagents. Nanotechnology 25: 225101. doi: 10.1088/0957-4484/25/22/225101
![]() |
[162] | Korehei R, Kadla J (2011) Incorporating pre-encapsulated bacteriophage in electrospun fibres, 16th International Symposium on Wood, Fiber and Pulping Chemistry, Tianjin, People’s Republic of China. Proceedings ISWFPC 2: 1302-1306. |
[163] |
Lee SW, Belcher AM (2004) Virus-based fabrication of micro- and nanofibers using electrospinning. J Nano Lett 4: 387-390. doi: 10.1021/nl034911t
![]() |
[164] |
Salalha W, Kuhn J, Dror Y, et al. (2006) Encapsulation of bacteria and viruses in electrospun nanofibers. Nanotechnology 17: 4675-4681. doi: 10.1088/0957-4484/17/18/025
![]() |
[165] |
Korehei R, Kadla J (2013) Incorporation of T4 bacteriophage in electrospun fibres. J Appl Microbiol 114: 1425-1434. doi: 10.1111/jam.12158
![]() |
1. | FeiMing Huang, Lei Chen, Wei Guo, Tao Huang, Yu-dong Cai, Fan Yang, Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods, 2022, 2022, 2314-6141, 1, 10.1155/2022/2516653 | |
2. | Yu-Hang Zhang, ShiJian Ding, Lei Chen, Tao Huang, Yu-Dong Cai, Andrey Cherstvy, Subcellular Localization Prediction of Human Proteins Using Multifeature Selection Methods, 2022, 2022, 2314-6141, 1, 10.1155/2022/3288527 | |
3. | Jian Lu, Mei Meng, XianChao Zhou, Shijian Ding, KaiYan Feng, Zhenbing Zeng, Tao Huang, Yu-Dong Cai, Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8+ T cells, 2022, 13, 1664-8021, 10.3389/fgene.2022.1053772 | |
4. | Yu-Hang Zhang, Zhan Dong Li, Tao Zeng, Lei Chen, Tao Huang, Yu-Dong Cai, Screening gene signatures for clinical response subtypes of lung transplantation, 2022, 297, 1617-4615, 1301, 10.1007/s00438-022-01918-x | |
5. | Wenjing Yi, Ao Sun, Manman Liu, Xiaoqing Liu, Wei Zhang, Qi Dai, Lin Lu, Comparative Study on Feature Selection in Protein Structure and Function Prediction, 2022, 2022, 1748-6718, 1, 10.1155/2022/1650693 | |
6. | ZhanDong Li, Deling Wang, HuiPing Liao, ShiQi Zhang, Wei Guo, Lei Chen, Lin Lu, Tao Huang, Yu-Dong Cai, Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method, 2022, 13, 1664-8021, 10.3389/fgene.2022.857851 | |
7. | Zhiyang Liu, Mei Meng, ShiJian Ding, XiaoChao Zhou, KaiYan Feng, Tao Huang, Yu-Dong Cai, Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods, 2022, 13, 1664-302X, 10.3389/fmicb.2022.1007295 | |
8. | Feiming Huang, Lei Chen, Wei Guo, Xianchao Zhou, Kaiyan Feng, Tao Huang, Yudong Cai, Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method, 2022, 12, 2075-1729, 806, 10.3390/life12060806 | |
9. | Qiao Sun, Lin Bai, Shaopin Zhu, Lu Cheng, Yang Xu, Yu-Dong Cai, Hui Chen, Jian Zhang, Ji-Fu Wei, Analysis of Lymphoma-Related Genes with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment, 2022, 2022, 2314-6141, 1, 10.1155/2022/8503511 | |
10. | Jiwei Song, FeiMing Huang, Lei Chen, KaiYan Feng, Fangfang Jian, Tao Huang, Yu-Dong Cai, Identification of methylation signatures associated with CAR T cell in B-cell acute lymphoblastic leukemia and non-hodgkin’s lymphoma, 2022, 12, 2234-943X, 10.3389/fonc.2022.976262 | |
11. | Yaochen Xu, FeiMing Huang, Wei Guo, KaiYan Feng, Lin Zhu, Zhenbing Zeng, Tao Huang, Yu-Dong Cai, Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution, 2023, 14, 1664-8021, 10.3389/fgene.2023.1145647 | |
12. | Xiaoqing Liu, Wenjing Yi, Baohang Xi, Qi Dai, Lin Lu, Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning, 2022, 2022, 1748-6718, 1, 10.1155/2022/7035634 | |
13. | Zhandong Li, Zi Mei, Shijian Ding, Lei Chen, Hao Li, Kaiyan Feng, Tao Huang, Yu-Dong Cai, Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods, 2022, 9, 2296-889X, 10.3389/fmolb.2022.908080 | |
14. | ZhanDong Li, Wei Guo, ShiJian Ding, Lei Chen, KaiYan Feng, Tao Huang, Yu-Dong Cai, Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods, 2022, 13, 1664-8021, 10.3389/fgene.2022.880997 | |
15. | Hao Li, Feiming Huang, Huiping Liao, Zhandong Li, Kaiyan Feng, Tao Huang, Yu-Dong Cai, Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method, 2022, 9, 2296-889X, 10.3389/fmolb.2022.952626 | |
16. | Jian Lu, JiaRui Li, Jingxin Ren, Shijian Ding, Zhenbing Zeng, Tao Huang, Yu-Dong Cai, Functional and embedding feature analysis for pan-cancer classification, 2022, 12, 2234-943X, 10.3389/fonc.2022.979336 | |
17. | ZhanDong Li, FeiMing Huang, Lei Chen, Tao Huang, Yu-Dong Cai, Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data, 2022, 10, 2296-4185, 10.3389/fbioe.2022.916309 | |
18. | Shiheng Lu, Hui Wang, Jian Zhang, Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores, 2022, 15, 1662-5099, 10.3389/fnmol.2022.1007352 | |
19. | Xianchao Zhou, Shijian Ding, Deling Wang, Lei Chen, Kaiyan Feng, Tao Huang, Zhandong Li, Yudong Cai, Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles, 2022, 12, 2075-1729, 550, 10.3390/life12040550 | |
20. | ZhanDong Li, Wei Guo, Tao Zeng, Jie Yin, KaiYan Feng, Tao Huang, Yu-Dong Cai, Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease, 2022, 16, 1662-453X, 10.3389/fnins.2022.895181 | |
21. | Zhandong Li, Xiaoyong Pan, Yu-Dong Cai, Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods, 2022, 10, 2296-4185, 10.3389/fbioe.2022.890901 | |
22. | Xiaohong Li, Xianchao Zhou, Shijian Ding, Lei Chen, Kaiyan Feng, Hao Li, Tao Huang, Yu-Dong Cai, Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods, 2022, 12, 2218-273X, 1735, 10.3390/biom12121735 | |
23. | Man Li, Xinyi Zhou, Siyao Qin, Ziyan Bin, Yanhui Wang, Improved RAkEL’s Fault Diagnosis Method for High-Speed Train Traction Transformer, 2023, 23, 1424-8220, 8067, 10.3390/s23198067 | |
24. | Hao Wang, Lei Chen, PMPTCE-HNEA: Predicting Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network Embedding Algorithm, 2023, 18, 15748936, 748, 10.2174/1574893618666230224121633 | |
25. | Lei Chen, Linyang Li, Prediction of Drug Pathway-based Disease Classes using Multiple Properties of Drugs, 2024, 19, 15748936, 859, 10.2174/0115748936284973240105115444 | |
26. | Jing-Xin Ren, Qian Gao, Xiao-Chao Zhou, Lei Chen, Wei Guo, Kai-Yan Feng, Lin Lu, Tao Huang, Yu-Dong Cai, Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes, 2023, 12, 2079-7737, 947, 10.3390/biology12070947 | |
27. | Lei Chen, Ruyun Qu, Xintong Liu, Improved multi-label classifiers for predicting protein subcellular localization, 2023, 21, 1551-0018, 214, 10.3934/mbe.2024010 | |
28. | Kailun Sun, Cornelis A. M. van Gestel, Hao Qiu, Two-Dimensional Layered Nano-MoS2 Induces Earthworm Immune Cell Apoptosis by Regulating Lysosomal Maintenance and Function: Toward Unbiased Screening and Validation of Suspicious Pathways, 2024, 58, 0013-936X, 19948, 10.1021/acs.est.4c04512 | |
29. | Lei Chen, Huiping Liao, Guohua Huang, Shijian Ding, Wei Guo, Tao Huang, Yudong Cai, Identification of DNA Methylation Signature and Rules for SARS-CoV-2 Associated with Age, 2022, 27, 2768-6701, 10.31083/j.fbl2707204 | |
30. | Yuanyuan Luo, Yihan Wang, Lin Liu, Feiming Huang, Shiheng Lu, Yan Yan, Identifying pathological myopia associated genes with GenePlexus in protein-protein interaction network, 2025, 16, 1664-8021, 10.3389/fgene.2025.1533567 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1 | 0.5329 | 0.2087 | 0.0777 | 0.1026 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01 | 0.4643 | 0.1441 | 0.0872 | 0.0611 |
Random forest | m = 10, k = 3, number of decision trees = 250 | 0.6025 | 0.2806 | 0.0687 | 0.1574 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, feature dimension = 300 | 0.5853 | 0.2407 | 0.0713 | 0.1308 |
Support vector machine (RBF kernel) |
m = 10, k = 3, C = 2, γ = 0.01, feature dimension=300 | 0.5020 | 0.1551 | 0.0824 | 0.0714 |
Random forest | m = 10, k = 5, number of decision trees = 250, feature dimension = 150 | 0.5727 | 0.2385 | 0.0714 | 0.1269 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.6242 | 0.2777 | 0.0660 | 0.1619 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension = 300 | 0.5439 | 0.2177 | 0.0743 | 0.1096 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.6235 | 0.2963 | 0.0633 | 0.1731 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.5059 | 0.1507 | 0.0781 | 0.0703 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension=300 | 0.4485 | 0.1112 | 0.0848 | 0.0456 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.5069 | 0.1608 | 0.0762 | 0.0753 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.6177 | 0.2705 | 0.0654 | 0.1562 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension=300 | 0.5427 | 0.2138 | 0.0737 | 0.1075 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.6195 | 0.2952 | 0.0635 | 0.1713 |
Index | Functional type |
Partition 1 | PROTEIN WITH BINDING FUNCTION OR COFACTOR REQUIREMENT (structural or catalytic) REGULATION OF METABOLISM AND PROTEIN FUNCTION CELLULAR COMMUNICATION/SIGNAL TRANSDUCTION MECHANISM SUBCELLULAR LOCALIZATION CELLULAR TRANSPORT, TRANSPORT FACILITIES AND TRANSPORT ROUTES TRANSCRIPTION ENERGY METABOLISM CELL CYCLE AND DNA PROCESSING PROTEIN FATE (folding, modification, destination) BIOGENESIS OF CELLULAR COMPONENTS SYSTEMIC INTERACTION WITH THE ENVIRONMENT PROTEIN SYNTHESIS CELL RESCUE, DEFENSE AND VIRULENCE |
Partition 2 | INTERACTION WITH THE ENVIRONMENT CELL TYPE LOCALIZATION TISSUE LOCALIZATION ORGAN LOCALIZATION TRANSPOSABLE ELEMENTS, VIRAL AND PLASMID PROTEINS |
Partition 3 | CELL FATE DEVELOPMENT (Systemic) TISSUE DIFFERENTIATION ORGAN DIFFERENTIATION CELL TYPE DIFFERENTIATION |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1 | 0.5329 | 0.2087 | 0.0777 | 0.1026 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01 | 0.4643 | 0.1441 | 0.0872 | 0.0611 |
Random forest | m = 10, k = 3, number of decision trees = 250 | 0.6025 | 0.2806 | 0.0687 | 0.1574 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, feature dimension = 300 | 0.5853 | 0.2407 | 0.0713 | 0.1308 |
Support vector machine (RBF kernel) |
m = 10, k = 3, C = 2, γ = 0.01, feature dimension=300 | 0.5020 | 0.1551 | 0.0824 | 0.0714 |
Random forest | m = 10, k = 5, number of decision trees = 250, feature dimension = 150 | 0.5727 | 0.2385 | 0.0714 | 0.1269 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.6242 | 0.2777 | 0.0660 | 0.1619 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension = 300 | 0.5439 | 0.2177 | 0.0743 | 0.1096 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.6235 | 0.2963 | 0.0633 | 0.1731 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.5059 | 0.1507 | 0.0781 | 0.0703 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension=300 | 0.4485 | 0.1112 | 0.0848 | 0.0456 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.5069 | 0.1608 | 0.0762 | 0.0753 |
Base classifier | Parameter | Accuracy | Exact match | Hamming loss | Integrated score |
Support vector machine (Polynomial kernel) |
m = 10, k = 5, C = 2, exponent = 1, network embedding feature dimension = 300 | 0.6177 | 0.2705 | 0.0654 | 0.1562 |
Support vector machine (RBF kernel) |
m = 10, k = 5, C = 2, γ = 0.01, network embedding feature dimension=300 | 0.5427 | 0.2138 | 0.0737 | 0.1075 |
Random forest | m = 10, k = 5, number of decision trees = 250, network embedding feature dimension = 150 | 0.6195 | 0.2952 | 0.0635 | 0.1713 |
Index | Functional type |
Partition 1 | PROTEIN WITH BINDING FUNCTION OR COFACTOR REQUIREMENT (structural or catalytic) REGULATION OF METABOLISM AND PROTEIN FUNCTION CELLULAR COMMUNICATION/SIGNAL TRANSDUCTION MECHANISM SUBCELLULAR LOCALIZATION CELLULAR TRANSPORT, TRANSPORT FACILITIES AND TRANSPORT ROUTES TRANSCRIPTION ENERGY METABOLISM CELL CYCLE AND DNA PROCESSING PROTEIN FATE (folding, modification, destination) BIOGENESIS OF CELLULAR COMPONENTS SYSTEMIC INTERACTION WITH THE ENVIRONMENT PROTEIN SYNTHESIS CELL RESCUE, DEFENSE AND VIRULENCE |
Partition 2 | INTERACTION WITH THE ENVIRONMENT CELL TYPE LOCALIZATION TISSUE LOCALIZATION ORGAN LOCALIZATION TRANSPOSABLE ELEMENTS, VIRAL AND PLASMID PROTEINS |
Partition 3 | CELL FATE DEVELOPMENT (Systemic) TISSUE DIFFERENTIATION ORGAN DIFFERENTIATION CELL TYPE DIFFERENTIATION |