Tuesday, October 5, 2021

Re: Speaker Invitation: Northwestern Pharmacology Symposium


In my talk, I will discuss interpretable machine learning models for
predicting the impact of genomic variants. These models focus on a
variety of different types of variants, from those in protein-coding
regions to those in non-coding regions, from those associated with
particular diseases, such as cancer or schizophrenia, to those having
a high impact in general, and from those involving single nucleotides
to those that are larger (structural variants). In particular, I will
describe physically based models for predicting cancer driver events,
simple statistical models for finding cancer non-coding drivers, and
interpretable deep learning models for mental disease. For the deep
learning models, I will show how the model's architecture relates to
the overall linear process of splicing or the comprehensive cellular
regulatory network. Finally, I will also highlight a general machine
learning approach for assessing the impact of structural variants.

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