[Colloquium] Eric Xing on Wednesday, March 3, 2004

[Margery Ishmael]

DEPARTMENT OF COMPUTER SCIENCE

Date: Wednesday, March 3, 2004
Time: 2:30 p.m.
Place: Ryerson 251

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Speaker:  ERIC XING,  University of California, Berkeley

Url:  http://www.cs.berkeley.edu/~epxing/

Title:  Probabilistic graphical models and algorithms for genomic 
analysis

Abstract:

I discuss two probabilistic modeling problems arising in metazoan 
genomic analysis: identifying motifs and cis-regulatory modules (CRMs) 
from transcriptional regulatory DNA sequences, and inferring haplotypes 
from genotypes of single nucleotide polymorphisms. Motif and CRM 
identification is important for understanding the gene regulatory 
network underlying metazoan development and functioning. I discuss a 
modular Bayesian model that captures rich structural characteristics of 
the transcriptional regulatory sequences and supports a variety of 
tasks such as learning motif representations, model-based motif and CRM 
prediction, and de novo motif detection. Haplotype inference is 
essential for the understanding of genetic variation within and among 
populations, with important applications to the genetic analysis of 
disease propensities and other complex traits. I discuss a Bayesian 
model based on a prior constructed from a Chinese restaurant process -- 
a nonparametric prior which provides control over the size of the 
unknown pool of population haplotypes, and on a likelihood that allows 
statistical errors in the haplotype/genotype relationship. Our models 
use the "probabilistic graphical model" formalism, a formalism that 
exploits the conjoined talents of graph theory and probability theory 
to build complex models out of simpler pieces. I discuss the 
mathematical underpinnings for the models, how they formally 
incorporate biological prior knowledge about the data, and the related 
computational issues.

Bio:

Eric Xing received his B.S. with honors in Physics and Biology from 
Tsinghua university, his Ph.D. in Molecular Biology and Biochemistry 
from Rutgers University and will soon complete his Ph.D. in Computer 
Science at UC Berkeley. His early work in molecular biology focused on 
the genetic mechanisms of human carcinogenesis and the mutational 
spectrum of tumor suppressor genes. Then he moved into machine learning 
and has worked on probabilistic graphical models, approximate inference 
and pattern recognition. He is interested in studying biological 
problems (in particular, systems biology, genetic inference and 
evolution) using statistical learning approaches, theory and 
application of graphical models, nonparametric Bayesian analysis and 
semi-unsupervised learning.

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Host: STUART KURTZ

***Refreshments will follow the talk in Ryerson 255***

People in need of assistance should call 773-834-8977 in advance.

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