Volume 11 Number 10 (Oct. 2016)
Home > Archive > 2016 > Volume 11 Number 10 (Oct. 2016) >
JSW 2016 Vol.11(10): 965-975 ISSN: 1796-217X
doi: 10.17706/jsw.11.10.965-975

A Deep Learning Approach to Detect SNP Interactions

Suneetha Uppu*, Aneesh Krishna*, Raj P. Gopalan
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.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
  • Dec 06, 2019 News!

    Vol 14, No 1- Vol 14, No 4 has been indexed by EI (Inspec)   [Click]

  • Jun 22, 2020 News!

    Papers published in JSW Vol 14, No 1- Vol 15 No 4 have been indexed by DBLP     [Click]

  • Sep 30, 2020 News!

    The papers published in Vol 15, No 6 have all received dois from Crossref   [Click]

  • Aug 01, 2018 News!

    [CFP] 2020 the annual meeting of JSW Editorial Board, ICCSM 2020, will be held in Rome, Italy, July 17-19, 2020   [Click]

  • Sep 30, 2020 News!

    Vol 15, No 6 has been published with online version     [Click]