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REVIEW ARTICLE
Year : 2020  |  Volume : 4  |  Issue : 1  |  Page : 8-15

Rheumatoid arthritis identification using epistasis analysis through computational models


1 Department of Computer Applications and Information Technology, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, India
2 Research Scholar, Computer Science, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, India

Correspondence Address:
Dr. R Manavalan
Assistant Professor in Computer Science, o/o Head, Department of Computer Applications and Information Technology, Arignar Anna Government Arts College, Villupuram - 605 602, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bbrj.bbrj_147_19

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Rheumatoid arthritis (RA) is an autoimmune disorder that damages joints irreversibly. Many RA illnesses were also related to complex genetic characteristics and genetic interactions as well. Genome-wide association studies (GWASs) analyzing the fundamental RA-related genetic factors over the past two decades. Nonlinear interaction recognition, also known as epistasis identification, plays a crucial part in identifying RA's genetic causes. GWAS recognizes all single nucleotide polymorphisms (SNPs) genetic variants and the interactions between SNPs to identify RA susceptibility. Manual evaluation and interactions of many SNPs were too complicated for physicians. The main objective of this study is to explore various techniques of statistical, machine learning, optimization, so far applied to identify epistasis effect related to arthritis. The challenges behind the computational model and the experimental outcome of various methods were also focused.


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