Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics,[1] including genomics, proteomics, microarrays, systems biology, evolution, and text mining.[2][3]
Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein structure prediction, this proved difficult.[4] Machine learning techniques, such as deep learning can learn features of data sets, instead of requiring the programmer to define them individually. The algorithm can further learn how to combine low-level features into more abstract features, and so on. This multi-layered approach allows such systems to make sophisticated predictions when appropriately trained. These methods contrast with other computational biology approaches which, while exploiting existing datasets, do not allow the data to be interpreted and analyzed in unanticipated ways.
^Chicco D (December 2017). "Ten quick tips for machine learning in computational biology". BioData Mining. 10 (35): 35. doi:10.1186/s13040-017-0155-3. PMC 5721660. PMID 29234465.
^Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, et al. (March 2006). "Machine learning in bioinformatics". Briefings in Bioinformatics. 7 (1): 86–112. doi:10.1093/bib/bbk007. PMID 16761367.
^Pérez-Wohlfeil E, Torrenoa O, Bellis LJ, Fernandes PL, Leskosek B, Trellesa O (December 2018). "Training bioinformaticians in High Performance Computing". Heliyon. 4 (12): e01057. Bibcode:2018Heliy...401057P. doi:10.1016/j.heliyon.2018.e01057. PMC 6299036. PMID 30582061.
^Yang Y, Gao J, Wang J, Heffernan R, Hanson J, Paliwal K, Zhou Y (May 2018). "Sixty-five years of the long march in protein secondary structure prediction: the final stretch?". Briefings in Bioinformatics. 19 (3): 482–494. doi:10.1093/bib/bbw129. PMC 5952956. PMID 28040746.
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