Department of Computing, Curtin University, Bentley 6102, Western Australia, Australia.

---

Abstract—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.

Index Terms—Deep feedforward neural networks, SNP-SNP interactions, two-locus polymorphisms, and machine learning techniques.

[PDF]

Cite: Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan, "A Deep Learning Approach to Detect SNP Interactions," Journal of Software vol. 11, no. 10, pp. 965-975, 2016.