[Sds-seminars] S&DS talk, Monday, 2/18, Marinka Zitnik, "Deep Learning for Network Biomedicine", DL 220

[Dan Spielman]

[image: Department of Statistics and Data Science]
<https://statistics.yale.edu/>  *Department of Statistics and Data Science *
<https://statistics.yale.edu/>

MARINKA ZITNIK, Stanford University

[image:
https://statistics.yale.edu/sites/default/files/styles/user_picture_node/public/marinka.png?itok=EcYdQA5Y]

Date:  Monday, February 18, 2019

Time:  4:00PM to 5:15PM

Location: Dunham Lab see map
<http://maps.google.com/?q=10+Hillhouse+Avenue%2C+Room+220%2C+New+Haven%2C+CT%2C+%2C+us>


10 Hillhouse Avenue, Room 220

New Haven, CT

Website <https://stanford.edu/~marinka/>







*Deep Learning for Network Biomedicine*



*Information and Abstract: *

Large datasets are being generated that can transform biology and medicine.
New machine learning methods are necessary to unlock these data and open
doors for scientific discoveries. In this talk, I will argue that, in order
to advance science, machine learning models should not be trained in the
context of one particular dataset. Instead, we should be developing methods
that can integrate rich, heterogeneous data and knowledge into multimodal
networks, enhance these networks to reduce biases and uncertainty, and
learn over the networks.

My talk will focus on two key aspects of this goal: deep learning and
network science for multimodal networks. I will first show how we can move
beyond prevailing deep learning methods, which treat network features as
simple variables and ignore interactions between entities. Further, I will
present an algorithm that learns deep models by embedding multimodal
networks into compact embedding spaces whose geometry is optimized to
reflect the interactions, the essence of multimodal networks. These deep
models set sights on new frontiers, including the prediction of protein
functions in specific human tissues, modeling of drug combinations, and
repurposing of old drugs for new diseases. Beyond such predictive ability,
a hallmark of science is to achieve a holistic understanding of the world.
I will discuss how we can blend network algorithms with rigorous statistics
to harness biomedical networks at the scale of billions of interactions.
These methods revealed, among others, how Darwinian evolution changes
molecular networks, providing evidence for a longstanding hypothesis in
biology. In all studies, I collaborated closely with experimental
biologists and clinical scientists to give insights and validate
predictions made by our methods. I will conclude with future directions for
contextual models of rich interaction data which open up new avenues for
science.

Bio: Marinka Zitnik (http://stanford.edu/~marinka/) is a postdoc in
Computer Science at Stanford University. Her research investigates machine
learning for biomedical sciences, focusing on new methods for large
networks of interactions between biomedical entities. Her methods have had
a tangible real-world impact in biology, genomics, and medicine, and are
used by major biomedical institutions, including Baylor College of
Medicine, Karolinska Institute, Stanford Medical School, and Massachusetts
General Hospital. She has multiple first-author papers in the top
scientific journals (PNAS, Nature Communications) as well as in the top
machine learning and computational biology venues (JMLR, NIPS, IEEE TPAMI,
KDD, Bioinformatics, ISMB, RECOMB). She received her Ph.D. in Computer
Science from University of Ljubljana while also researching at Imperial
College London, University of Toronto, Baylor College of Medicine, and
Stanford University. Her work received several best paper, poster, and
research awards from the International Society for Computational Biology.
She was selected a Google Anita Borg Scholar, Young Fellow at Heidelberg
Laureate Forum, and received Jozef Stefan Golden Emblem Prize. In 2018, she
was named a Rising Star in EECS by MIT and also a Next Generation in
Biomedicine by The Broad Institute of Harvard and MIT, being the only young
scientist who received such recognition in both EECS and Biomedicine. She
is also a member of the Chan Zuckerberg Biohub at Stanford.

3:45pm - Pre-talk tea, Dunham Lab., Suite 222, Room 228

For more details and upcoming events visit our website at
http://statistics.yale.edu/ .



Department of Statistics and Data Science

Yale University
24 Hillhouse Avenue
New Haven, CT 06511

t 203.432.0666
f 203.432.0633




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