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Machine Learning Techniques for Analysis of Human Genome Data
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Author(s): Neelambika Basavaraj Hiremath (Department of Computer Science and Engineering, J.S.S. Academy of Technical Education Bengaluru, India)and Dayananda P. (Department of Information Science and Engineering. J.S.S. Academy of Technical Education, Bengaluru, India)
Copyright: 2019
Volume: 10
Issue: 1
Pages: 15
Source title:
International Journal of Smart Education and Urban Society (IJSEUS)
Editor(s)-in-Chief: Linda Daniela (University of Latvia, Latvia)
DOI: 10.4018/IJSEUS.2019010105
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Abstract
Human genome data analysis is one of the molecular level information in health informatics, which enables genetic epidemiological analysis of complex data sets. The recent studies of the genomic sequence, a part of genome-wide association studies (GWAS) have led to understand the genetic architecture to identify the area of focus i.e. interactions with single-nucleotide polymorphism (SNP) is linked to causing complex diseases. The study and identification of these interactions and splicing of nucleic acids involves complexity in processing and computation. This article reviews current methods and trends in various machine learning and data mining approaches which are very complex and challenging to model and evaluate the performances.
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