Machine Learning Approaches Towards System Level Understanding of Gene Regulation and Gene Function

Speaker:	Dr. Renqiang Min
		Yale University

Title:		"Machine Learning Approaches Towards System Level
		 Understanding of Gene Regulation and Gene Function"

Date:		Monday, 4 April 2011

Time:		4:00pm - 5:00pm

Venue:		Lecture Theatre F (near lifts 25/26), HKUST

Abstract:

With the availability of high-throughput genomics, functional genomics,
and proteomics data during the last decade, elucidating the underlying
mechanisms of gene regulation and gene function at a system level becomes
possible, which helps to understand the molecular mechanisms of human
diseases. In this talk, I will present machine learning approaches to
predicting gene regulation and gene function at different biological
levels. First, at gene expression and protein product level, I will show
how hierarchical Bayesian graphical models were used to infer the
post-transcriptional regulatory mechanisms of small non-coding RNAs called
microRNAs, by integrating sequence data, expression data, and proteomics
data. Second, at protein sequence level, I will show how the functions of
genes can be predicted directly from protein sequences in SCOP, using
learned random-walk kernels and learned empirical-map kernels. Last, at
cellular morphology level, I will present how kernel methods and deep
embedding methods based on undirected graphical models were constructed to
characterize novel functions of yeast essential genes, by analyzing the
microscopy image data from genome-wide high-content screening of yeast
temperature-sensitive mutants.

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Biography:

Renqiang (Martin) Min is a postdoctoral associate working with Prof. Mark
Gerstein in Program of Computational Biology and Bioinformatics, Yale
University. He received his Master and PhD degrees in Computer Science
from Machine Learning Group, Department of Computer Science, University of
Toronto, and obtained his Bachelor degree in Computer Science from Nankai
University. His research focuses on bioinformatics and machine learning,
in particular, personal genomics, sequence analysis, gene regulation
inference, gene function prediction, kernel methods, graphical models, and
deep learning.