Computational Molecular Biology 2024, Vol.14, No.2, 76-83 http://bioscipublisher.com/index.php/cmb 82 integration of deep learning with other advanced technologies, such as CRISPR and single-cell sequencing, can lead to breakthroughs in understanding complex biological systems and developing targeted therapies (Cao et al., 2020; Karim et al., 2020). By expanding the applications of deep learning, researchers can unlock new insights and drive innovation in bioinformatics and related fields. Acknowledgments I would like to express my special thanks to Professor Shakti from Hainan University for providing in-depth guidance during the research process. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Auslander N., Gussow A., and Koonin E., 2021, Incorporating machine learning into established bioinformatics frameworks, International Journal of Molecular Sciences, 22(6): 2903. https://doi.org/10.3390/ijms22062903 Berrar D., and Dubitzky W., 2021, Deep learning in bioinformatics and biomedicine, Briefings in Bioinformatics, 22(2): 1513-1514. https://doi.org/10.1093/bib/bbab087 Cao C.S., Liu F., Tan H., Song D.S., Shu W.J., Li W.Z., Zhou Y.M., Bo X.C., and Xie Z., 2018, Deep learning and its applications in biomedicine, Genomics, Proteomics and Bioinformatics, 16(1): 17-32. https://doi.org/10.1016/j.gpb.2017.07.003 Cao Y., Geddes T.A., Yang J.Y., and Yang P.Y., 2020, Ensemble deep learning in bioinformatics, Nature Machine Intelligence, 2(9): 500-508. https://doi.org/10.1038/s42256-020-0217-y Gauthier J., Vincent A.T., Charette S.J., and Derome N., 2019, A brief history of bioinformatics, Briefings in Bioinformatics, 20(6): 1981-1996. https://doi.org/10.1093/bib/bby063 Goh G.B., Hodas N.O., and Vishnu A., 2017, Deep learning for computational chemistry, Journal of Computational Chemistry, 38(16): 1291-1307. https://doi.org/10.1002/jcc.24764 Jin S., Zeng X., Xia F., Huang W., and Liu X., 2020, Application of deep learning methods in biological networks, Briefings in Bioinformatics, 22(2): 1902-1917. https://doi.org/10.1093/bib/bbaa043 Karim M., Beyan O., Zappa A., Costa I., Rebholz-Schuhmann D., Cochez M., and Decker S., 2020, Deep learning-based clustering approaches for bioinformatics, Briefings in Bioinformatics, 22(1): 393-415. https://doi.org/10.1093/bib/bbz170 Koumakis L., 2020, Deep learning models in genomics; are we there yet, Computational and Structural Biotechnology Journal, 18: 1466-1473. https://doi.org/10.1016/j.csbj.2020.06.017 Lan K., Wang D.T., Fong S., Liu L.S., Wong K.K.L., and Dey N.J., 2018, A survey of data mining and deep learning in bioinformatics, Journal of Medical Systems, 42: 1-20. https://doi.org/10.1007/s10916-018-1003-9 LeCun Y., Bengio Y., and Hinton G., 2015, Deep learning, Nature, 521: 436-444. https://doi.org/10.1038/nature14539 Li H.Y., Tian S.Y., Li Y., Fang Q.M., Tan R.B., Pan Y.J., Huang C., Xu Y., and Gao X., 2020, Modern deep learning in bioinformatics, Journal of Molecular Cell Biology, 12(11): 823-827. https://doi.org/10.1093/jmcb/mjaa030 Li Y., Huang C., Ding L., Li Z., Pan Y., and Gao X., 2019, Deep learning in bioinformatics: introduction, application, and perspective in the big data era, Methods, 166: 4-21. https://doi.org/10.1016/J.YMETH.2019.04.008 Libbrecht M.W., and Noble W.S,, 2015, Machine learning applications in genetics and genomics, Nature Reviews Genetics, 16(6): 321-332. https://doi.org/10.1038/nrg3920 Liu W.B., Wang Z.D., Liu X.H., Zeng N.Y., Liu Y.R., and Alsaadi F.E., 2017, A survey of deep neural network architectures and their applications, Neurocomputing, 234: 11-26. https://doi.org/10.1016/j.neucom.2016.12.038 Mamoshina P., Vieira A., Putin E., and Zhavoronkov A., 2016, Applications of deep learning in biomedicine, Molecular Pharmaceutics, 13(5): 1445-1454. https://doi.org/10.1021/acs.molpharmaceut.5b00982 Min S., Lee B., and Yoon S., 2016, Deep learning in bioinformatics, Briefings in Bioinformatics, 18(5): 851-869. https://doi.org/10.1093/bib/bbw068
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