A survey of machine learning techniques for genomic diseases and data sets

  • Authors

    • Manu Phogat Guru Jambheshwar University of Science and Technology, Hisar, India, 125001
    • Dr. Dharmender Kumar Guru Jambheshwar University of Science and Technology, Hisar, India, 125001
    https://doi.org/10.14419/ijet.v7i4.11016

    Received date: April 3, 2018

    Accepted date: July 2, 2018

    Published date: April 7, 2019

  • Machine Learning, ANN, KNN, RF, SVM, Genomic, Mutation.

  • Abstract

    From the very early age of Medical Science, medical practitioners have been concerned about visualizing and analyzing complex biological data which was not so easy. Today is the era of GWAS (genome-wide association studies), so the quest for understanding the genotype of various complex diseases is rapidly increasing day by day. Recently, high throughput molecular data have provided ample information about the whole genome, and have popularized the computational tools in genomics. Due to the humongous size and high dimensionality of genomic data, it is not possible to analyze it with conventional techniques, so machine learning tends to develop efficient computational techniques that will raise with experience, for analysis the vast complex data sets. This article give an outline of different machine learning techniques for examination of the genomics data of diseases and epigenetic, proteomic data.

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    Phogat, M., & Dharmender Kumar, D. (2019). A survey of machine learning techniques for genomic diseases and data sets. International Journal of Engineering and Technology, 7(4), 5533-5538. https://doi.org/10.14419/ijet.v7i4.11016