Discovering Higher-order SNP Interactions in High-dimensional Genomic Data
In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning tech...
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| Format: | Thesis |
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Curtin University
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/77025 |
| _version_ | 1848763800905318400 |
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| author | Uppu, Suneetha |
| author_facet | Uppu, Suneetha |
| author_sort | Uppu, Suneetha |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise. |
| first_indexed | 2025-11-14T11:09:13Z |
| format | Thesis |
| id | curtin-20.500.11937-77025 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:09:13Z |
| publishDate | 2018 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-770252019-11-28T02:43:48Z Discovering Higher-order SNP Interactions in High-dimensional Genomic Data Uppu, Suneetha In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise. 2018 Thesis http://hdl.handle.net/20.500.11937/77025 Curtin University fulltext |
| spellingShingle | Uppu, Suneetha Discovering Higher-order SNP Interactions in High-dimensional Genomic Data |
| title | Discovering Higher-order SNP Interactions in High-dimensional Genomic Data |
| title_full | Discovering Higher-order SNP Interactions in High-dimensional Genomic Data |
| title_fullStr | Discovering Higher-order SNP Interactions in High-dimensional Genomic Data |
| title_full_unstemmed | Discovering Higher-order SNP Interactions in High-dimensional Genomic Data |
| title_short | Discovering Higher-order SNP Interactions in High-dimensional Genomic Data |
| title_sort | discovering higher-order snp interactions in high-dimensional genomic data |
| url | http://hdl.handle.net/20.500.11937/77025 |