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Prioritization of mutations in coding and noncoding regions of the human genome using machine learning approaches

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Abstract: With over 3-5 million variants between any two human genomes it is difficult to identify and prioritize variants involved in a particular physiological condition. The problem is not trivial with 90% of variants mapping to noncoding regions, which may or may not have biological functions attributed to them. In this talk, I will discuss existing frameworks and algorithms that aid in prioritizing variants in both coding and noncoding regions and discuss the challenges we face. I will also discuss some aspects of our own work in developing classifiers to prioritize variants in certain regions of the noncoding genome.

This talk is part of the Computational and Systems Biology series.

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