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Feature selection and classification approaches in gene expression of breast cancer

  • DNA microarray technology with biological data-set can monitor the expression levels of thousands of genes simultaneously. Microarray data analysis is important in phenotype classification of diseases. In this work, the computational part basically predicts the tendency towards mortality using different classification techniques by identifying features from the high dimensional dataset. We have analyzed the breast cancer transcriptional genomic data of 1554 transcripts captured over from 272 samples. This work presents effective methods for gene classification using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and constructs a classifier with an upgraded rate of accuracy than all features together. The performance of these underlying methods are also compared with dimension reduction method, namely, Principal Component Analysis (PCA). The methods of feature reduction with RF, LR and decision tree (DT) provide better performance than PCA. It is observed that both techniques LR and RF identify TYMP, ERS1, C-MYB and TUBA1a genes. But some features corresponding to the genes such as ARID4B, DNMT3A, TOX3, RGS17 and PNLIP are uniquely pointed out by LR method which are leading to a significant role in breast cancer. The simulation is based on R-software.

    Citation: Sarada Ghosh, Guruprasad Samanta, Manuel De la Sen. Feature selection and classification approaches in gene expression of breast cancer[J]. AIMS Biophysics, 2021, 8(4): 372-384. doi: 10.3934/biophy.2021029

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  • DNA microarray technology with biological data-set can monitor the expression levels of thousands of genes simultaneously. Microarray data analysis is important in phenotype classification of diseases. In this work, the computational part basically predicts the tendency towards mortality using different classification techniques by identifying features from the high dimensional dataset. We have analyzed the breast cancer transcriptional genomic data of 1554 transcripts captured over from 272 samples. This work presents effective methods for gene classification using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and constructs a classifier with an upgraded rate of accuracy than all features together. The performance of these underlying methods are also compared with dimension reduction method, namely, Principal Component Analysis (PCA). The methods of feature reduction with RF, LR and decision tree (DT) provide better performance than PCA. It is observed that both techniques LR and RF identify TYMP, ERS1, C-MYB and TUBA1a genes. But some features corresponding to the genes such as ARID4B, DNMT3A, TOX3, RGS17 and PNLIP are uniquely pointed out by LR method which are leading to a significant role in breast cancer. The simulation is based on R-software.



    The Faber-Krahn inequality, named also the Rayleigh-Faber-Krahn inequality, states that the ball minimizes the fundamental eigenvalue of the Dirichlet Laplacian among bounded domains with fixed volume. It was conjectured by Lord Rayleigh [1] and then proved independently by Faber [2] and Krahn [3]. One of the extensions of this inequality is the result established by B. Schwarz [4] for nonhomogeneous membranes, which is stated as follows: Let λ1(p) be principal frequency of a nonhomogeneous membrane D with positive density function p, then

    λ1(p)λ1(p), (1.1)

    where p is the Schwartz symmetrization of p and λ1(p) is the first eigenvalue of the symmetrized problem in the disk D. Our aim in this paper is to give a version of the B. Schwarz inequality for the case of bounded domains completely contained in a wedge of angle πα,α1. Such bounded domains are called wedge-like membranes. The method for proving our result requires a weighted version of decreasing rearrangement tailored to the case of wedge-like membranes. This technique was first introduced by Payne and Weinberger [5], and then studied and used to improve many classical inequalities by several authors, see for example [6,7,8,9,10]. An interesting feature of this method is that it leads to an improvement of classical inequalities for certain domains, as shown by Payne and Weiberger [5] and Hasnaoui and Hermi [11]. Also, the results of this method have the interpretation of being the usual results in dimension d=2α+2 for domains with axial or bi-axial symmetry, see [9,11,12]. We are also interested in a new version of the Banks-Krein inequality [13] where the numerical value of the lower bound of the first eigenvalue is given by the first positive root of an equation involving the Bessel function Jα.

    Before stating our results, we need to introduce some notations and preliminary tools. Letting α1, we will denote by W the wedge defined in polar coordinates (r,θ) in R2 by

    W={(r,θ)|0<r,0<θ<πα}. (2.1)

    For τ0, we define the sector of radius τ by

    Sτ={(r,θ)|0<r<τ,0<θ<πα}. (2.2)

    We also define

    h(r,θ)=rαsinαθ, (2.3)

    which is a positive harmonic function in W vanishing on the boundary W. From that, we introduce the weighted measure μ defined by

    μ(D)=Ddμ=Dh2dx, (2.4)

    for all bounded domains DW.

    Throughout this paper, we denote by λ1(w) the first eigenvalue of the problem

    P1:{Δu+λwu=0 in Du=0 on D,

    where D is a bounded domain completely contained in W and w is a positive continuous function on D. It has been shown that this problem has a countably infinite discrete set of positive eigenvalues, and the first eigenvalue λ1(w) is simple and has an eigenfunction u of constant sign, see for example [14]. We will assume that u>0 in D. Hence, the first eigenfunction can be represented as

    u=vh, (2.5)

    where v is a positive smooth function vanishing on DW.

    Now, we introduce the weighted rearrangement with respect to the measure μ, which is one of the principal tools in our work. Let f be a measurable function defined in DW, and let Sr0 be the sector of radius r0 such that

    μ(Sr0)=μ(D).

    Furthermore, we denote the sector with the same measure as a measurable subset A of D by A. The distribution function of f with respect to the measure μ is defined by

    mf(t)=μ({(r,θ)D;|f(r,θ)|>t}),t[0,ess sup|f|]. (2.6)

    The decreasing rearrangement of f with respect to μ is given by

    f(0)=ess sup|f|,
    f(s)=inf{t0;mf(t)<s},s(0,μ(D)].

    The weighted rearrangement of f is the function f defined on the sector Sr0 by

    f(r,θ)=f(μ(Sr)). (2.7)

    An explicit computation gives that μ(Sr)=π4α(α+1)r2α+2. Substituting this in (2.7), we obtain

    f(r,θ)=f(π4α(α+1)r2α+2). (2.8)

    Since f is a radial and nonincreasing, it follows that its level sets are sectors centered at the origin and have weighted measure equal to mf(t). We will, by abuse of notation, write f(r) instead of f(r,θ). Recall that w is the density of the membrane D. Let w denotes the weighted rearrangement of w, and λ1(w) denotes the lowest eigenvalue of the following symmetrized problem

    P2:{Δz+λwz=0 in Sr0z=0 on Sr0.

    The following result compares the first eigenvalue of the problem P1 with that of the symmetrized problem P2.

    Theorem 2.1. If w is a positive continuous function defined on DW, then

    λ1(w)λ1(w). (2.9)

    See the following section for a proof of the theorem. Note that Theorem 2.1 includes the Payne-Weinberger inequality [5] as the special case w=1. The result above is also a new version of the B. Schwarz inequality [4] for wedge-like membranes.

    To state the second result in this paper, we need to assume that there is a real number P such that 0wP. From that, we introduce the function ˉw defined in Sr0 by

    ˉw(r,θ)={P, for r[0,ρ],0, for r(ρ,r0],

    where ρ is chosen such that Sr0wdμ=Sr0ˉwdμ.

    Theorem 2.2. Assume that 0wP. The first eigenvalue of the problem P2 satisfies the inequality

    λ1(w)λ1(ˉw), (2.10)

    where λ1(ˉw) is the first eigenvalue of the problem

    P3:{Δψ+λˉwψ=0 inSr0ψ=0onSr0.

    The proof of Theorem 2.2 is detailed in the third section. In fact, this result together with the corollary below extend the Banks-Krein theorem [13] to the case of wedge like membranes. The classical version of our result was first proved for vibrating strings by Krein [15] and then extended to planar domains by Banks [13].

    Corollary 2.3. Let 0wP. Then,

    λ1(w)λ1(ˉw), (2.11)

    where λ1(ˉw) is the first positive solution of the equation

    Jα(λ1(ˉw)Pρ)+λ1(ˉw)PραJα(λ1(ˉw)Pρ)1(ρr0)2α1+(ρr0)2α=0. (2.12)

    See the third section for a proof of the corollary.

    At the end of this section, we give an appropriate variational characterization to the eigenvalue λ1(w) for the case of wedge-like domains. To begin, consider the functional space W(D,dμ) which is the set of measurable functions ϕ satisfying the following conditions:

    (ⅰ) D|ϕ|2dμ+D|ϕ|2dμ<+.

    (ⅱ) There exists a sequence of functions ϕnC1(¯D) such that ϕn=0 on DW and

    limn+D|(ϕϕn)|2dμ+D|ϕϕn|2dμ=0.

    For more details about this space, see [9].

    Lemma 2.4. The first eigenvalue of the problem P1 can be defined via the weighted variational characterization

    λ1(w)=minϕW(D,dμ)D|ϕ|2dμDwϕ2dμ. (2.13)

    The proof of Lemma 2.4 is detailed in the following section.

    In this part, we will prove Theorem 2.1 by showing that the weighted symmetrization decreases the numerator and increases the denominator of the Rayleigh quotient. For the numerator, we have the following weighted version of the Pólya-Szegő inequality.

    Proposition 3.1. Let f be a nonnegative function in W(D,dμ). Then, fW(Sr0,dμ), and

    D|f|2dμSr0|f|2dμ. (3.1)

    The complete and detailed proof of Proposition 3.1 is given in [9] for more general cases dμ=hkdx,k>1. For the denominator, we need the following lemma.

    Lemma 3.2. Let D be a bounded domain completely contained in Wand f be a μ-integrable function defined in D. Let Ω be a measurable subset of D. Then,

    ΩfdμSr1fdμ, (3.2)

    where Sr1 is the sector satisfying μ(Sr1)=μ(Ω).

    Proof. If g denotes the restriction of f to Ω, we have

    mg(t)=μ({(r,θ)D;|f(r,θ)|>t}Ω).

    Thus, if s[mf(t),μ(Ω)], then mg(t)<s. Hence,

    {t0;mf(t)<s}{t0;mg(t)<s} (3.3)

    and so

    inf{t0;mg(t)<s}inf{t0;mf(t)<s}, (3.4)

    which is exactly the inequality g(s)f(s).

    Thus,

    Ωfdμ=Ωgdμ=μ(Ω)0g(s)dsμ(Ω)0f(s)ds. (3.5)

    Now, by the change of variable s=π4α(α+1)r2α+2, we have

    Sr1fdμ=r10πα0f(π4α(α+1)r2α+2)r2α+1sin2αθdrdθ (3.6)
    =π2αr10f(π4α(α+1)r2α+2)r2α+1dr (3.7)
    =μ(Sr1)0f(s)ds (3.8)
    =μ(Ω)0f(s)ds, (3.9)

    which proves the lemma.

    Now, we are finally in a position to complete the proof of Theorem 2.1. Recall the function v defined by (2.5). Let 0c0wc1 and χΩ denotes the characteristic function of a subset Ω of the domain D. Then,

    Dwv2dμ=Dv2h2c10χ{w>t}dtdx (3.10)
    =c10Dv2χ{w>t}h2dxdt (3.11)
    =c00Dv2χ{w>t}h2dxdt+c1c0Dv2χ{w>t}h2dxdt (3.12)
    =c0Dv2h2dx+c1c0{w>t}v2h2dxdt. (3.13)

    By Lemma 3.2 and the fact that Dv2h2dx=Sr0(v)2h2dx, we obtain

    Dwv2dμc0Sr0(v)2h2dx+c1c0{w>t}(v2)h2dxdt. (3.14)

    Now, using the equalities (v)2=(v2) and {w>t}={w>t} in the second term on the right-hand side of the last inequality, we deduce that

    Dwv2dμc0Sr0(v)2h2dx+c1c0{w>t}(v)2h2dxdt (3.15)
    =Sr0w(v)2dμ. (3.16)

    The last equality was obtained by applying the same computation in (3.10) to v and w. Finally, using Proposition 3.1 and inequality (3.15), we obtain that vW(Sr0,dμ) and

    λ1(w)=D|v|2dμDwv2dμSr0|v|2dμSr0w(v)2dμminϕW(Sr0,dμ)Sr0|ϕ|2dμSr0wϕ2dμ=λ1(w).

    The proof of Theorem 2.1 is now complete.

    The following lemmas are essential for the proof of our theorem.

    Lemma 3.3. Let f1, f2, and Φ be μ-integrable functions over D, let Ω1={(r,θ)D|f1(r,θ)f2(r,θ)} and Ω2={(r,θ)D|f1(r,θ)>f2(r,θ)}, and suppose

    Df1dμDf2dμ. (3.17)

    If 0Φ(r1,θ1)Φ(r2,θ2) for all (r1,θ1)Ω1, (r2,θ2)Ω2, then

    Df1ΦdμDf2Φdμ. (3.18)

    The proof of this lemma is similar to the proof of Lemma 2.7 in [16].

    Lemma 3.4. The eigenfunction z1 corresponding to the first eigenvalue λ1(w) of the problem P2 can be written as z1=ξh, where ξ is a radial function, which is radially decreasing.

    Proof. By Proposition 3.1 and inequality (3.15), it follows that

    λ1(w)=Sr0|ξ|2dμSr0wξ2dμSr0|ξ|2dμSr0w(ξ)2dμ. (3.19)

    Since ξW(Sr0,dμ), then ξW(Sr0,dμ) and is an admissible function for the weighted variational formula (2.13). Using this and inequality (3.19), we see

    λ1(w)=Sr0|ξ|2dμSr0w(ξ)2dμ, (3.20)

    which means that ξh is an eigenfunction as well. Finally, the simplicity of the first eigenvalue λ1(w) implies that z1=ξh=ξh, and so ξ=ξ. Thus, ξ is radial and radially decreasing. This completes the proof of the lemma.

    Now, setting Ω1={(r,θ)Sr0|ρ<r<r0,0<θ<πα} and Ω2={(r,θ)Sr0|0<r<ρ,0<θ<πα}, it is not difficult to check that the functions ˉw and w satisfy the same relationship as f1 and f2 of Lemma 3.3. Also, using Lemma 3.4, we obtain that ξ satisfies the same assumption as Φ, and then

    Sr0wξ2dμSr0ˉwξ2dμ. (3.21)

    Using the above inequality, we obtain

    λ1(w)=Sr0|ξ|2dμSr0wξ2dμSr0|ξ|2dμSr0ˉwξ2dμminϕW(Sr0,dμ)Sr0|ϕ|2dμSr0ˉwϕ2dμ=λ1(ˉw).

    This completes the proof of the theorem.

    Inequality (2.11) follows immediately from Theorems 2.1 and 2.2. To prove the equality (2.12), we first proceed as in [8] to obtain that the eigenfunction corresponding to λ1(ˉw) is explicitly given by ψ1(r,θ)=R(r)sinαθ, where the function R is defined on [0,r0] by

    R(r)={cJα(λ1(ˉw)Pr), for r[0,ρ],˜c(rαr2α0rα), for r(ρ,r0].

    Here, the constants c and ˜c satisfy the continuity of the function R and of its derivative. Now, since R is continuous at r=ρ, we see that

    cJα(λ1(ˉw)Pρ)=˜c(ραr2α0ρα). (3.22)

    The continuity of the derivative of R at r=ρ gives

    cλ1(ˉw)PJα(λ1(ˉw)Pρ)=˜cαρ(ρα+r2α0ρα). (3.23)

    Thus,

    ˜c=ραλ1(ˉw)PJα(λ1(ˉw)Pρ)1ρα+r2α0ρα. (3.24)

    Finally, plugging Eq (3.24) into (3.22), we obtain the desired result. The proof of Corollary 2.3 is now complete.

    The first eigenvalue of problem P1 can be characterized by the Rayleigh principle

    λ1(w)=minφH10(D)D|φ|2dxDwφ2dx. (3.25)

    Let ϕW(D,dμ). Using the fact that Δh=0 and the divergence theorem, we obtain

    D|(ϕh)|2dx=D|ϕ|2h2+|h|2ϕ2+2ϕhhϕdx=D|ϕ|2h2dx. (3.26)

    Since the function ϕh belongs to the Sobolev space H10(D), then we can use it as a test function in the Rayleigh quotient (3.25). Applying (3.26), we get

    λ1(w)D|(ϕh)|2dxDwϕ2h2dx=D|ϕ|2dμDwϕ2dμ. (3.27)

    Now, if we write the first eigenfunction as in (2.5) and substitute it into (3.25), we obtain

    λ1(w)=D|u|2dxDwu2dx=D|(vh)|2dxDwv2h2dx=D|v|2h2dxDwv2h2dx=D|v|2dμDwv2dμ,

    which proves the lemma.

    The Dirichlet eigenvalues are known only for a limited number of regions, such as disks, sectors and rectangles. This lack of information has prompted many researchers to explore methods and techniques for estimating eigenvalues. In this paper, we have proved a new lower bound for the first Dirichlet eigenvalue of an arbitrarily shaped region with continuous mass density function and completely contained in a wedge. This lower bound has been given as the lowest positive root of the Eq (2.12). In our next projects, we aim to use the method of wedge like-membranes to improve the Z. Nehari inequality [17]. Additionally, We will adapt the increasing rearrangement techniques to offer a complementary results to those of this paper. Furthermore, the generalization of all these results to higher dimensions will be considered in future works.

    The authors contributed equally and they both read and approved the final manuscript for publication.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number "NBU-FPEJ-2025-2941-01".

    The authors declare that there is no conflict of interest.


    Acknowledgments



    The authors are grateful to the learned reviewers and Editors for their careful reading, valuable comments and helpful suggestions, which have helped them to improve the presentation of this work significantly. The third author (Manuel De la Sen) is grateful to the Spanish Government for its support through grant RTI2018-094336-B-I00 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19.

    Data availability statement



    The data used to support the findings of this study are included in the references within the article.

    Conflict of interest



    The authors declare that they have no conflict of interest regarding this work.

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