carnegie mellon

Description

Slides: http://lyra.berkeley.edu/CDIConf/pdfs/MLBerkeleyBarJoseph.pdf Ziv Bar-Joseph: "Data integration for modeling dynamic biological systems". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012). Abstract Ziv Bar-Joseph (Computer Science Dept., Carnegie Mellon University) Several recent technological advances are transforming molecular biology to a data intensive field. Several different types of data, each providing a different point of view of cellular activity, can now be measured on a large scale. These include sequencing data, mRNA and microRNA expression data and various types of interaction datasets. While valuable, these datasets also raise several computational challenges and machine learning methods have been playing an ever increasing role in addressing them. In this talk I will discuss some challenges associated with the analysis of sequencing data from next generation machines that often generate tens of millions of (often noisy) short reads. I will then discuss how to integrate time series data from these next generation RNA-Sequencing studies with (mostly static) protein-DNA interaction data for modeling dynamic regulatory networks using an Input-Output Hidden Markov model (IOHMM). These network models lead to testable temporal hypotheses identifying both new regulators and their time of activation. Our models can be extended to integrate other types of biological interactions including protein interactions (for modeling signaling networks) and microRNA data. I will discuss the application and experimental validation of predictions made by our methods and mplications for predicting interventions in various diseases.