CoMeT | <%=title %>
Collaborative Management of Talks Hello! sign in or register
Bookmark Talks, Share with Friends, and We Recommend More!
Advanced Search
Talk Detail
Posted: tls18 on  Apr 15 06:20:41 AM
Title: Instance-Specific Causal Bayesian Network Learning  
Gregory F. Cooper, MD, PhD
University of Pittsburgh
Host: Department of Biomedical Informatics
Sponsor: University of Pittsburgh
University of Pittsburgh  >  School of Medicine  >  Biomedical Informatics
Series: DBMI Lecture Series
Date: Apr 19, 2019 11:00 AM - 12:00 PM
Location: 407A/B BAUM, Offices at Baum, 5607 Baum Blvd. or weblink in description

This talk describes an instance-specific causal Bayesian network (CBN) learning method that searches the space of CBNs to build a causal model that is specific to an instance (e.g., a patient). The search is guided by attributes of the given instance (e.g., patient symptoms, signs, lab results, and genotype). We describe the results of applying the method to molecular cancer data to estimate the gene alterations (e.g., gene mutations) that are driving the cancerous behavior of individual tumors, which are the instances in this application. Follow up biological experiments provide support that the method is able to identify new genomic drivers of cancer.


Join form computer or phone:

Phone Dial-in
+1.888.240.2560 (US Toll Free)

Meeting ID: 148 972 068

Interest Area: Arts and Humanities, Biological Sciences, Business and Industry, Chemistry, Computer & Information Science & Engineering, Education, Engineering, General Interests, Geosciences, Health Sciences, Law, Mathematical & Physical Sciences, Social, Behavioral & Economic Sciences
People Who Viewed This Talk, Also Viewed
RSS Feed: RSS 2.0
ATOM Feed: Atom
iCalendar: iCal
Share: Bookmark and Share
Google Calendar:
CoMeT Blog
©2009-2019 CoMeT - Supported by Google Grant
School of Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA 15260
Real Time Web Analytics