An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome
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 numb...
| Main Authors: | , , |
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| Format: | Conference Paper |
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IEEE
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/39547 |
| _version_ | 1848755620182753280 |
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| author | Uppu, S. Krishna, Aneesh Gopalan, Raj |
| author2 | Xingquan (Hill) Zhu |
| author_facet | Xingquan (Hill) Zhu Uppu, S. Krishna, Aneesh Gopalan, Raj |
| author_sort | Uppu, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:59:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-39547 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:59:12Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-395472023-02-27T07:34:32Z An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome Uppu, S. Krishna, Aneesh Gopalan, Raj Xingquan (Hill) Zhu Reda Alhajj Taghi M. Khoshgoftaar Nikolaos G. Bourbakis associative classification SNP-SNP interactions Epistasis multi-locus 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. 2014 Conference Paper http://hdl.handle.net/20.500.11937/39547 10.1109/BIBE.2014.29 IEEE fulltext |
| spellingShingle | associative classification SNP-SNP interactions Epistasis multi-locus Uppu, S. Krishna, Aneesh Gopalan, Raj An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome |
| title | An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome |
| title_full | An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome |
| title_fullStr | An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome |
| title_full_unstemmed | An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome |
| title_short | An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome |
| title_sort | associative classification based approach for detecting snp-snp interactions in high dimensional genome |
| topic | associative classification SNP-SNP interactions Epistasis multi-locus |
| url | http://hdl.handle.net/20.500.11937/39547 |