title: Prioritisation of candidate Single Amino Acid Polymorphisms using one-class learning machines.
abstract: Recent advancements of the next-generation 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 non-functional 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 one-class 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 one-class learning methods to prioritise candidate SAAPs. Systematic validation demonstrates that the proposed model is effective in recovering disease-associated SAAPs.
authors.affiliation: Department of Automation, Tsinghua University, Beijing, China.
authors.forename: Jiaxin
authors.initials: J
authors.lastname: Wu
authors.forename: Mingxin
authors.initials: M
authors.lastname: Gan
authors.forename: Rui
authors.initials: R
authors.lastname: Jiang
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000235:genetics*; D014644:Genetic Variation; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011110:Polymorphism, Genetic; D025341:Principal Component Analysis; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids