文章基本信息

标题：A Deep Learning Approach to Detect SNP Interactions
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作者：Suneetha Uppu ; Aneesh Krishna ; Raj P. Gopalan 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2016
卷号：11
期号：10
页码：965-975
DOI：10.17706/jsw.11.10.965-975
出版社：Academy Publisher
摘要：The susceptibility of complex diseases are characterised by numerous genetic, lifestyle, and environmental causes individually or due to their interaction effects. The recent explosion in detecting genetic interacting factors is increasingly revealing the underlying biological networks behind complex diseases. Several computational methods are explored to discover interacting polymorphisms among unlinked loci. However, there has been no significant breakthrough towards solving this problem because of biomolecular complexities and computational limitations. Our previous research trained a deep multilayered feedforward neural network to predict two-locus polymorphisms due to interactions in genome-wide data. The performance of the method was studied on numerous simulated datasets and a published genomewide dataset. In this manuscript, the performance of the trained multilayer neural network is validated by varying the parameters of the models under various scenarios. Furthermore, the observations of the previous method are confirmed in this study by evaluating on a real dataset. The experimental findings on a real dataset show significant rise in the prediction accuracy over other conventional techniques. The result shows highly ranked interacting two-locus polymorphisms, which may be associated with susceptibility for the development of breast cancer.
其他关键词：Deep feedforward neural networks, SNP-SNP interactions, two-locus polymorphisms, and machine learning techniques.