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DTSTART;TZID=America/New_York:20190411T150000
DTEND;TZID=America/New_York:20190411T160000
DTSTAMP:20260408T224127
CREATED:20190320T170132Z
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UID:1503-1554994800-1554998400@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: " Deep Learning for Network Biomedicine"
DESCRIPTION:Abstract:\n\nLarge datasets are being generated that can transform biology and medicine. New machine learning methods are necessary to \nunlock these data and open doors for scientific discoveries. In this talk\, I will argue that\, in order to advance science\, \nmachine learning models should not be trained in the context of one particular dataset. Instead\, we should be developing \nmethods that can integrate rich\, heterogeneous data and knowledge into multimodal networks\, enhance these networks to reduce \nbiases and uncertainty\, and learn over the networks. \n\nMy talk will focus on two key aspects of this goal: deep learning and network science for multimodal networks. I will first \nshow how we can move beyond prevailing deep learning methods\, which treat network features as simple variables and ignore \ninteractions between entities. Further\, I will present an algorithm that learns deep models by embedding multimodal networks \ninto compact embedding spaces whose geometry is optimized to reflect the interactions\, the essence of multimodal networks. \nThese deep models set sights on new frontiers\, including the prediction of protein functions in specific human tissues\, \nmodeling of drug combinations\, and repurposing of old drugs for new diseases. Beyond such predictive ability\, a hallmark of \nscience is to achieve a holistic understanding of the world. I will discuss how we can blend network algorithms with rigorous \nstatistics to harness biomedical networks at the scale of billions of interactions. These methods revealed\, among others\, how \nDarwinian evolution changes molecular networks\, providing evidence for a longstanding hypothesis in biology. In all studies\, I \ncollaborated closely with experimental biologists and clinical scientists to give insights and validate predictions made by our \nmethods. I will conclude with future directions for contextual models of rich interaction data which open up new avenues for science.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-deep-learning-for-network-biomedicine/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
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