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Towards Machine Learning in Molecular Biology

1 Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, China
2 Department of Electrical Engineering and Computer Science, and Bond Life Science Center, University of Missouri, USA

Special Issues: Machine Learning in Molecular Biology

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1. H. Zhu, N. Wang, J. Z. Sun, R. B. Pandey, Z. Wang, Inferring the three-dimensional structures of the X-chromosome during X-inactivation, Math. Biosci. Eng.,16 (2019), 7384-7404.

2. S. J. Lu, J. Xie, Y. Li, B. Yu, Q. Ma, B. Q. Liu, Identification of lncRNAs-gene interactions in transcription regulation based on co-expression analysis of RNA-seq data, Math. Biosci. Eng.,16 (2019), 7112-7125.

3. P. P. Sun, Y. B. Chen, B. Liu, Y. X. Gao, Y. Han, F. He, et al., DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning, Math. Biosci. Eng.,16 (2019), 6231-6241.

4. L. Huang, S. Y. Guo, Y. Wang, S. Wang, Q. B. Chu, L. Li, et al., Attention based residual network for medicinal fungi near infrared spectroscopy analysis, Math. Biosci. Eng.,16 (2019), 3003-3017.

5. J. X. Tan, S. H. Li, Z. M. Zhang, C. X. Chen, W. Chen, H. A. Tang, et al., Identification of hormone binding proteins based on machine learning methods, Math. Biosci. Eng.,16 (2019), 2466-2480.

6. Y. Sun, W. Du, L. L. Yang, M. Dai, Z. Y. Dou, Y. X. Wang, et al., Computational methods for recognition of cancer protein markers in saliva, Math. Biosci. Eng.,17 (2020), 2453-2469.

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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