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Computational approaches for deciphering the regulation of cancer genomes

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If you have a question about this talk, please contact Mohammad Taher Pilehvar.

Cancer consists of a collection of more than 200 different diseases and arise due to aberrant changes in the genome. Even within a single cancer type these aberrant mutations can be dissimilar and lead to tumour heterogeneity. Rapid technological advances in the last decade has given us the ability to “read” the genome sequence quickly and cheaply. Consequently, a number of large public cancer genome sequencing efforts have resulted in useful insights into carcinogenesis and treatment. The current state of cancer genomics will be briefly introduced.

My lab utilises computational biology and data science approaches to understand carcinogenic processes and systems involved in cancer. This includes developing bespoke computational methods, tools and resources that are applied to different biomedical data types. We’re particularly interested in using these different functional genomic data layers to understand their joint influence on rules underlying gene and epigenome regulation and how these are perturbed in cancer. Some of our data integration methods (including our foray into text mining and natural language processing) and resources used for building insightful maps and models of the cellular regulatory landscape will be discussed.

This talk is part of the Language Technology Lab Seminars series.

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