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dc.contributor.authorUppu, S.
dc.contributor.authorKrishna, Aneesh
dc.contributor.authorGopalan, Raj
dc.contributor.editorXingquan (Hill) Zhu, Reda Alhajj, Taghi M. Khoshgoftaar, and Nikolaos G. Bourbakis
dc.date.accessioned2017-01-30T14:34:57Z
dc.date.available2017-01-30T14:34:57Z
dc.date.created2015-05-22T08:32:23Z
dc.date.issued2014
dc.identifier.citationUppu, S. and Krishna, A. and Gopalan, R. 2014. An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome, in 14th Ieee International Conference on Bioinformatics and Bioengineering, Nov 10-12 2014. Boca Raton, Florida, USA: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/39547
dc.identifier.doi10.1109/BIBE.2014.29
dc.description.abstract

There have been many studies that depict genotype phenotype relationships by identifying genetic variants associated with a specific disease. Researchers focus more attention on interactions between SNPs that are strongly associated with disease in the absence of main effect. In this context, a number of machine learning and data mining tools are applied to identify the combinations of multi-locus SNPs in higher order data.However, none of the current models can identify useful SNPSNP interactions for high dimensional genome data. Detecting these interactions is challenging due to bio-molecular complexities and computational limitations. The goal of this research was to implement associative classification and study its effectiveness for detecting the epistasis in balanced and imbalanced datasets. The proposed approach was evaluated for two locus epistasis interactions using simulated data. The datasets were generated for 5 different penetrance functions by varying heritability, minor allele frequency and sample size. In total, 23,400 datasets were generated and several experiments are conducted to identify the disease causal SNP interactions. The accuracy of classification by the proposed approach wascompared with the previous approaches. Though associative classification showed only relatively small improvement in accuracy for balanced datasets, it outperformed existing approaches in higher order multi-locus interactions in imbalanced datasets.

dc.publisherIEEE
dc.subjectassociative classification
dc.subjectSNP-SNP interactions
dc.subjectEpistasis
dc.subjectmulti-locus
dc.titleAn associative classification based approach for detecting SNP-SNP interactions in high dimensional genome
dc.typeConference Paper
dcterms.source.startPage329
dcterms.source.endPage333
dcterms.source.title14th Ieee International Conference on Bioinformatics and Bioengineering proceedings
dcterms.source.series14th Ieee International Conference on Bioinformatics and Bioengineering proceedings
dcterms.source.isbn978-1-4799-7501-3
dcterms.source.conference14th Ieee International Conference on Bioinformatics and Bioengineering
dcterms.source.conference-start-dateNov 10 2014
dcterms.source.conferencelocationBoca Raton, Florida, USA
dcterms.source.placeUnited States
curtin.note

Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

curtin.departmentDepartment of Computing
curtin.accessStatusOpen access


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