talk: An Integrated Machine Learning Framework for...

Full Title: talk: An Integrated Machine Learning Framework for Analyzing Protein-Ligand Interaction Data

CSEE Colloquium

An Integrated Machine Learning Framework for Analyzing Protein-Ligand Interaction Data

Dr. Huzefa Rangwala
Assistant Professor, Computer Science & Engineering
George Mason University

1:00 p.m., Friday, February 10, 2012, ITE 325B, UMBC

 

Proteins have a vast influence on the molecular machinery of life. Stunningly complex networks of proteins perform innumerable functions in every living cell.  Small organic molecules (a.k.a. ligands) can bind to different proteins and modulate (inhibit/activate) their functions. Understanding these interactions provides insight into the underlying biological processes and is useful for designing therapeutic drugs.

In this talk I will describe our work related to the analysis of information associated with proteins and their interacting molecule partners (protein-ligand activity matrix). The underlying hypothesis of our approach is that by extracting information from protein-ligand activity matrix, we are drawing bridges between the structure of chemical compounds (chemical space) and the structure of the proteins and their functions (biological space).  I will present an approach used for mining relational data, especially when the data is sparse and high dimensional. I will also present methods that are based on the principles of multi-task learning and semi-supervised learning.

Huzefa Rangwala is an Assistant Professor at the department of Computer Science & Engineering, George Mason University. He holds affiliate positions with the Department of Bioengineering and the Department of Bioinformatics & Computational Biology. He received his Ph.D. in Computer Science from the University of Minnesota in the year 2008. His core research interests include bioinformatics, machine learning, and high performance computing. Specifically, he is working on developing new data mining algorithms and applying them to the fields of genomics, structural bioinformatics, drug discovery and social media analysis.

Host: Dr. Marie desJardins

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