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- W2074080357 abstract "Recent advancements of the nextgeneration sequencing technology have enabled the direct sequencing of rare genetic variants in both case and control individuals. Although there have been a few statistical methods for uncovering potential associations between multiple rare variants and human inherited diseases, most of these methods require computational approaches to filter out nonfunctional variants for the purpose of maximising the statistical power. To tackle this problem, we formulate the detection of genetic variants that are associated with a specific type of disease from the perspective of oneclass novelty learning. We focus on a typical type of genetic variants called Single Amino Acid Polymorphisms (SAAPs), and we take advantages of a feature selection mechanism and two oneclass learning methods to prioritise candidate SAAPs. Systematic validation demonstrates that the proposed model is effective in recovering diseaseassociated SAAPs." @default.
- W2074080357 created "2016-06-24" @default.
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- W2074080357 date "2011-01-01" @default.
- W2074080357 modified "2023-10-16" @default.
- W2074080357 title "Prioritisation of candidate Single Amino Acid Polymorphisms using one-class learning machines" @default.
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- W2074080357 doi "https://doi.org/10.1504/ijcbdd.2011.044446" @default.
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