Genomics and machine learning

R. Velmurugan, Phong Thanh Nguyen, E. Laxmi Lydia, K. Shankar, Wahidah Hashim, Andino Maseleno

Research output: Contribution to journalArticle


Genomics is one of the most focused area for studying and helps to understand the nature of disease and it is an area where genetics can be deeply studied and research conclusion can be obtained. Genomics is different from genetics as genetics is the composition of only single gene but on the opposite side the genomics contains all gens and also keep track of their collectively growth during the development process of an organism. Here the datasets of DNA on the organism is called Genomic data. This datasets are further used in bioinformatics for doing experiments on collect and process for research. For this purpose a very large storage space and specifically-built computer program is required to analyze. Genomic is also different from the proteomics because in proteomics only focuses on the proteins present in the cell. The Genomics research involves many scientific factors, which leads to identify many diseases symptoms such as heart related disease, diabetic, cancer etc. Here in this approach genomics is useful because somewhere and somehow the genetic and the external factors are causing such diseases. The purpose of deep learning with genomics is to identify the disease and learning the development structure of disease. Such research may help in treating diseases in a better way.

Original languageEnglish
Pages (from-to)414-417
Number of pages4
JournalInternational Journal of Engineering and Advanced Technology
Issue number5 Special Issue 3
Publication statusPublished - Jul 2019

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Engineering(all)
  • Computer Science Applications

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  • Cite this

    Velmurugan, R., Nguyen, P. T., Laxmi Lydia, E., Shankar, K., Hashim, W., & Maseleno, A. (2019). Genomics and machine learning. International Journal of Engineering and Advanced Technology, 8(5 Special Issue 3), 414-417.