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CIS Seminar: ” Deep Learning for Network Biomedicine”

April 11 at 3:00 PM - 4:00 PM

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.

Marinka Zitnik

Computer Science Department, Stanford University

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.

Details

Date:
April 11
Time:
3:00 PM - 4:00 PM
Event Tags:
Website:
http://www.cis.upenn.edu/about-cis/events/index.php

Venue

Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street
Philadelphia, PA 19104 United States
+ Google Map
Website:
https://www.facilities.upenn.edu/maps/locations/levine-hall-melvin-and-claire-weiss-tech-house