<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Wednesday, February 16th at<b> <span style="background-color:rgb(255,255,0)">11:30 am CT</span></b></font></font><br></font></p><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b><span style="background-color:rgb(255,255,0)"><br></span></b></font></font></font></p><div class="gmail_default"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font style="color:rgb(80,0,80)">Zoom Virtual <span class="gmail-il">Talk</span> (</font><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_JFaznBfdRSe3KcvNoN78Dg" target="_blank"><font color="#0000ff">register in advance here</font></a></b><font style="color:rgb(80,0,80)">)</font><br></font></div><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><font color="#000000"><b>Who: </b> </font><font color="#500050"> </font><font color="#000000"> </font></font></font><span class="gmail-il">Sai</span> <span class="gmail-il">Zhang</span>, Stanford University</font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 8pt;line-height:normal"><font face="arial, sans-serif"><b>Title:</b> Machine learning for decoding complex human diseases</font></p><p class="MsoNormal" style="margin:0in 0in 8pt;line-height:normal"><font face="arial, sans-serif"><b>Abstract:</b> In the era of big biomedical data, millions of genomes have been sequenced which provides an unprecedented opportunity to systematically investigate the molecular components underlying complex diseases. However, the complexity and heterogeneity of the biological data substantially challenge traditional methodologies for effective analysis and discovery. In this <span class="gmail-il">talk</span>, I will introduce my effort on developing novel machine learning algorithms for disease genome analysis. First, I will describe RefMap, a Bayesian network that pinpoints disease risk genes by integrating genetic data with epigenetic profiling. The increased discovery power of RefMap has been demonstrated in several diseases including amyotrophic lateral sclerosis, severe COVID-19, and preterm birth. Next, I will discuss a series of models that leverage techniques in probabilistic graphical models and deep learning to predict disease risk from personal genomes. I will also present successful applications of these models on several diseases such as abdominal aortic aneurysm, thoracic aortic dissection, and cardiomyopathy. I will conclude my <span class="gmail-il">talk</span> with future plans on building data-driven frameworks to assist mechanism discovery, therapeutic development, and clinical decision making.</font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"></p><p class="MsoNormal" style="margin:0in 0in 8pt;line-height:normal"><font face="arial, sans-serif"><b>Bio:</b> <span class="gmail-il">Sai</span> <span class="gmail-il">Zhang</span> (<a href="https://sai-zhang.com/" target="_blank">https://<span class="gmail-il">sai</span>-<span class="gmail-il">zhang</span>.com/</a>) is an Instructor in the Department of Genetics at Stanford University School of Medicine. He received postdoctoral training in Dr. Michael Snyder’s group at Stanford Genetics from 2017 to 2021. Prior to that, he got a Ph.D. in Computer Science from Tsinghua University in 2017. His main research focus is the development of machine learning algorithms (e.g., deep learning and probabilistic graphical models) which exploit massive genetic, multiomic, and clinical data to uncover the genomic basis of complex human diseases. The long-term goal of his research is to build advanced artificial intelligence systems to assist scientific discovery, clinical decision making, and personal health management. As the first author, <span class="gmail-il">Sai</span> has published multiple research papers in top journals such as Cell, Neuron, and Cell Systems, as well as in top conferences in computational biology such as RECOMB and ISMB. Some of his studies have been highlighted by Nature, Nature Reviews Neurology, NIH Research Matters, etc.</font></p></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:j3xu@ttic.edu" target="_blank"><b>Jinbo Xu</b></a></font></div><div><br></div><div><br></div><div><br></div></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Feb 10, 2022 at 8:22 AM Mary Marre <<a href="mailto:mmarre@ttic.edu">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Wednesday, February 16th at<b> <span style="background-color:rgb(255,255,0)">11:30 am CT</span></b></font></font><br></font></p><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;color:rgb(80,0,80);margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b><span style="background-color:rgb(255,255,0)"><br></span></b></font></font></font></p><div><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font style="color:rgb(80,0,80)">Zoom Virtual Talk (</font><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_JFaznBfdRSe3KcvNoN78Dg" target="_blank"><font color="#0000ff">register in advance here</font></a></b><font style="color:rgb(80,0,80)">)</font><br></font></div><p class="MsoNormal" style="margin:0in 0in 0.0001pt;color:rgb(80,0,80);line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><font color="#000000"><b>Who: </b> </font><font color="#500050"> </font><font color="#000000"> </font></font></font>Sai Zhang, Stanford University</font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><br></font></p><p class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt"><font face="arial, sans-serif"><b>Title:</b> Machine
learning for decoding complex human diseases </font></p><p class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt"><font face="arial, sans-serif"><b>Abstract:</b> In the
era of big biomedical data, millions of genomes have been sequenced which provides
an unprecedented opportunity to systematically investigate the molecular
components underlying complex diseases. However, the complexity and
heterogeneity of the biological data substantially challenge traditional
methodologies for effective analysis and discovery. In this talk, I will
introduce my effort on developing novel machine learning algorithms for disease
genome analysis. First, I will describe RefMap, a Bayesian network that
pinpoints disease risk genes by integrating genetic data with epigenetic
profiling. The increased discovery power of RefMap has been demonstrated in
several diseases including amyotrophic lateral sclerosis, severe COVID-19, and
preterm birth. Next, I will discuss a series of models that leverage techniques
in probabilistic graphical models and deep learning to predict disease risk
from personal genomes. I will also present successful applications of these
models on several diseases such as abdominal aortic aneurysm, thoracic aortic
dissection, and cardiomyopathy. I will conclude my talk with future plans on
building data-driven frameworks to assist mechanism discovery, therapeutic
development, and clinical decision making. </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">
</p><p class="MsoNormal" style="line-height:normal;margin:0in 0in 8pt"><font face="arial, sans-serif"><b>Bio:</b> Sai Zhang (<a href="https://sai-zhang.com/" target="_blank">https://sai-zhang.com/</a>) is
an Instructor in the Department of Genetics at Stanford University School of
Medicine. He received postdoctoral training in Dr. Michael Snyder’s group at
Stanford Genetics from 2017 to 2021. Prior to that, he got a Ph.D. in Computer
Science from Tsinghua University in 2017. His main research focus is the development
of machine learning algorithms (e.g., deep learning and probabilistic graphical
models) which exploit massive genetic, multiomic, and clinical data to uncover
the genomic basis of complex human diseases. The long-term goal of his research
is to build advanced artificial intelligence systems to assist scientific
discovery, clinical decision making, and personal health management. As the
first author, Sai has published multiple research papers in top journals such
as Cell, Neuron, and Cell Systems, as well as in top conferences in
computational biology such as RECOMB and ISMB. Some of his studies have been
highlighted by Nature, Nature Reviews Neurology, NIH Research Matters, etc.</font></p></div><div><font face="arial, sans-serif"><br></font></div><div><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:j3xu@ttic.edu" target="_blank"><b>Jinbo Xu</b></a></font></div><div><br></div><div><br></div><div><br></div><div><br></div></div><div><div dir="ltr"><div dir="ltr"><div><span style="font-family:arial,helvetica,sans-serif;font-size:x-small">Mary C. Marre</span><br></div><div><div><font face="arial, helvetica, sans-serif" size="1">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6" size="1">6045 S. Kenwood Avenue</font></i></div><div><font size="1"><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i><br></font></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif" size="1">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div>
</blockquote></div></div>