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BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH CALSCALE:GREGORIAN PRODID:-//WordPress - MECv6.5.5//EN X-ORIGINAL-URL:https://oursymposium.sites.unlv.edu/ X-WR-CALNAME:Office of Undergraduate Research (OUR) Fall 2021 Virtual Undergraduate Research Symposium X-WR-CALDESC: REFRESH-INTERVAL;VALUE=DURATION:PT1H X-PUBLISHED-TTL:PT1H X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VEVENT CLASS:PUBLIC DTSTART;TZID=UTC:20211115T000000 DTEND;TZID=UTC:20211120T000000 DTSTAMP:20211028T000000 UID:MEC-42a6845a557bef704ad8ac9cb4461d43@oursymposium.sites.unlv.edu CREATED:20211028 LAST-MODIFIED:20211115 PRIORITY:5 TRANSP:OPAQUE SUMMARY:HNSE-P4-8. Predicting Variant Pathogenicity with Machine Learning DESCRIPTION:\n\n\nZachary FitzHugh1, 2\nFatma Nasoz, Ph.D.3\nFaculty Mentor: Martin Schiller, Ph.D.4\n1Howard R. Hughes College of Engineering, Department of Computer Science\n2Lee Business School, Department of Economics\n3The Lincy Institute\n4College of Sciences, Nevada Institute of Personalized Medicine\nABSTRACT\nThere are roughly 22,000 protein-coding genes in the human body, many of which play important roles in biological functions. The proteins fold in 3D space, and this is most often necessary for function. A genetic variant can disrupt the secondary structure of a protein (one aspect of structure) or eliminate a site important in protein-protein interaction or post-translational modification. The loss of function or deregulation can result in disease. Thus, there is great biomedical interest in identifying disease-causing single-nucleotide variants.\nWe hypothesize that we can accurately predict variant pathogenicity. We used machine learning to predict the pathogenicity of a set of 28,369 single-nucleotide variants across 10 genes. The data are acquired from publicly available saturation mutagenesis data sets, which generate every possible amino acid substitution at every position in a protein. Our approach employs a support vector machine using linear, polynomial, and RBF kernel functions. The problem is implemented as a binary classification problem, where a label of 1 indicates a disease-causing variant and a label of 0 indicates a benign variant. The model predicts pathogenicity based on amino acid, post-translational modification, and secondary structure information. We cleaned and analyzed the data with custom Python scripts. Our results show average balanced accuracy scores for classifying pathogenicity of approximately 57.9%, 60.3%, and 60.3% for the linear, polynomial, and RBF kernels, respectively. Therefore, the model is an improvement over random guessing but has room for improvement.\n URL:https://oursymposium.sites.unlv.edu/program/predicting-variant-pathogenicity-with-machine-learning/ ORGANIZER;CN=The Office of Undergraduate Research:MAILTO:our@unlv.edu CATEGORIES:Health & Natural Sciences & Engineering END:VEVENT END:VCALENDAR