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dc.contributor.authorUppu, S.
dc.contributor.authorKrishna, Aneesh
dc.identifier.citationUppu, S. and Krishna, A. 2016. Improving strategy for discovering interacting genetic variants in association studies, International Conference on Neural Information Processing - ICONIP 2016: Neural Information Processing, pp. 461-469. Champagne, Ill.: Springer.

Revealing the underlying complex architecture of human diseases has received considerable attention since the exploration of genotype-phenotype relationships in genetic epidemiology. Identification of these relationships becomes more challenging due to multiple factors acting together or independently. A deep neural network was trained in the previous work to identify two-locus interacting single nucleotide polymorphisms (SNPs) related to a complex disease. The model was assessed for all two-locus combinations under various simulated scenarios. The results showed significant improvements in predicting SNP-SNP interactions over the existing conventional machine learning techniques. Furthermore, the findings are confirmed on a published dataset. However, the performance of the proposed method in the higher-order interactions was unknown. The objective of this study is to validate the model for the higher-order interactions in high-dimensional data. The proposed method is further extended for unsupervised learning. A number of experiments were performed on the simulated datasets under same scenarios as well as a real dataset to show the performance of the extended model. On an average, the results illustrate improved performance over the previous methods. The model is further evaluated on a sporadic breast cancer dataset to identify higher-order interactions between SNPs. The results rank top 20 higherorder SNP interactions responsible for sporadic breast cancer.

dc.titleImproving strategy for discovering interacting genetic variants in association studies
dc.typeConference Paper
dcterms.source.volume9947 LNCS
dcterms.source.titleICONIP 2016: Neural Information Processing
dcterms.source.seriesLecture Notes in Computer Science; 9947.
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available

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