Title: "Learning drug and disease networks from single cell data"
Speaker: Karen Sachs, Stanford University School of Medicine, Stanford, CA

Place: SMITH (SMTH) Hall 108
Date: April 8, 2014; Tuesday
Time: 4:30pm

High throughput, (relatively) high dimensional single cell data provides a uniquely powerful opportunity for studying molecular pathways of the underlying biological system. In this talk, I will describe some of the methods that we are developing to help extract information from these rich datasets. Our methods include approaches for extracting signaling regulatory information by learning the joint probability distribution and representing it with a Bayesian network model; a method that helps in visualization and organization of high dimensional data, and an approach for in silico further expanding the number of parameters available. This talk will be accessible to both a biological as well as a statistical audience.

Associated reading:
Karen Sachs, Omar Perez, Dana Pe'er, Douglas A. Lauffenburger, and Garry P. Nolan, Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data, Science 22 April 2005; 308: 523-529

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