A Study on machine learning methods and applications in genetics and genomics

  • Authors

    • K Jayanthi
    • C Mahesh
    2018-02-05
    https://doi.org/10.14419/ijet.v7i1.7.10653
  • Machine Learning Methods, Genomics Classification Problems, Future Application Of Genomics.

  • Abstract

    Machine learning enables computers to help humans in analysing knowledge from large, complex data sets. One of the complex data is genetics and genomic data which needs to analyse various set of functions automatically by the computers. Hope this machine learning methods can provide more useful for making these data for further usage like gene prediction, gene expression, gene ontology, gene finding, gene editing and etc. The purpose of this study is to explore some machine learning applications and algorithms to genetic and genomic data. At the end of this study we conclude the following topics classifications of machine learning problems: supervised, unsupervised and semi supervised, which type of method is suitable for various problems in genomics, applications of machine learning and future views of machine learning in genomics.

  • References

    1. [1] Mitchell, T. Machine Learning. McGraw-Hill; 1997.

      [2] Ohler W, Liao C, Niemann H, Rubin GM. Computational analysis of core promoters in the drosophila genome. GenomeBiology. 2002;

      [3] Picardi E, Pesole G. Computational methods for ab initio and comparative gene finding. Methods in Molecular Biology. 2010; 609:269–284. [PubMed: 20221925]

      [4] Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics. 2000; 25:25–29. [PubMed: 10802651]

      [5] Beer MA, Tavazoie S. Predicting gene expression from sequence. Cell. 2004; 117:185–198. [PubMed: 15084257]

      [6] Friedman N. Inferring cellular networks using probabilistic graphical models. Science. 2004; 303:799–805. [PubMed: 14764868]

      [7] Day N, Hemmaplardh A, Thurman RE, Stamatoyannopoulos JA, Noble WS. Unsupervised segmentation of continuous genomic data. Bioinformatics. 2007; 23:1424–1426. [PubMed: 17384021]

      [8] Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012; 9:215–216. [PubMed: 22373907]

      [9] Hoffman MM, et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods. 2012; 9:473–476. [PubMed: 22426492]

      [10] Lanckriet GRG, Bie TD, Cristianini N, Jordan MI, Noble WS. A statistical framework for genomic data fusion. Bioinformatics. 2004; 20:2626–2635. [PubMed: 15130933]

      [11] W. Libbrecht “Machine learning in genetics and genomics Maxwell†Nat Rev Genet. Author manuscript; available in PMC 2017 January 02.

      [12] http://graveleylab.cam.uchc.edu/WebData/mduff/MEDS_6498_SPRING_2016/machine_learning_genomics_Noble_2015.pdf

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  • How to Cite

    Jayanthi, K., & Mahesh, C. (2018). A Study on machine learning methods and applications in genetics and genomics. International Journal of Engineering & Technology, 7(1.7), 201-204. https://doi.org/10.14419/ijet.v7i1.7.10653

    Received date: 2018-03-26

    Accepted date: 2018-03-26

    Published date: 2018-02-05