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From Genetics to Epigenetics to CRISPR Gene Editing with Machine Learning

Thursday, October 20, 2016 - 15:00 to 16:00
Jennifer Listgarten
Statistics Seminar
Michael Smith Labs, UBC, auditorium room (#102)

Abstract: Molecular biology, healthcare and medicine have been slowly morphing
into large-scale, data driven sciences dependent on machine learning and
applied statistics. In this talk I will start by explaining some of the
modelling challenges in finding the genetic underpinnings of disease, which is
important for screening, treatment, drug development, and basic biological
insight. Genome and epigenome-wide associations, wherein individual or sets of
(epi)genetic markers are systematically scanned for association with disease
are one window into disease processes. Naively, these associations can be
found by use of a simple statistical test. However, a wide variety of
structure and confounders lie hidden in the data, leading to both spurious and
missed associations if not properly addressed. Most of this talk will focus on
how to model these types of data. Once we uncover genetic causes, genome
editing—which is about deleting or changing parts of the genetic code—will one
day let us fix the genome in a bespoke manner. Editing will also help us to
understand mechanisms of disease, enable precision medicine and drug
development, to name just a few more important applications. I will close by
discussing how we are using machine learning to enable more effective CRISPR
gene editing.

Bio: Jennifer Listgarten is a Senior Researcher at Microsoft Research New
England, located in Cambridge, MA. She took a long and winding road to find her
current area of interest in computational biology, starting off with an
undergraduate degree in Physics, followed by a Master’s in Computer Vision
before completing a Ph.D. in Machine Learning at the University of Toronto.
Her current focus is in machine learning and applied statistics with
application to problems in biology. She works on both methods development and
applications enabling new insights into basic biology and medicine. Particular
areas of focus have included CRISPR guide design, statistical genetics,
immunoinformatics, liquid-chromatography proteomics, and microarray analysis.
You can find out more about her work here: