Dr. Jin Chen
Pheno-informatics: A New Framework for Analyzing Phenomics Data
Nowadays, DNA sequence data are available for many species, but the systematic quantification and analysis of phenotypes remains a big challenge. My research aim is to bridge the genotype-phenotype gap by developing novel data mining techniques. So that multi-omics data can be transformed into testable hypotheses to identify important genes in various aspects. In this talk, I will first introduce our recent progress in phenomics data modeling, including a new inter-functional phenomics clustering method and a new phenotype-environment relationship learning framework. I will illustrate how these tools have allowed us to discover new mechanism of photosynthesis in plants. In the second part, I will discuss our future plan in bioinformatics and data science, and their applications in biomedical research.
Bio: Dr. Jin Chen obtained his PhD in Computer Science from the National University of Singapore, School of Computing in 2007. He did his postdoc training in Carnegie Institution, Stanford from 2007 to 2009. After that, he joined the Michigan State University as Assistant Professor. His research focuses on developing novel data mining, artificial intelligence and computer vision algorithms to solve basic biological problems. With supports from NSF and DOE, his group has developed a dozen of pheno-informatics tools with the aim of solving the world food shortage problem.
Thursday, February 18, 2016 at 12:00 pm to 1:00 pm
SEM Scrugham Engineering & Mines, 261
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College of Engineering, Computer Science and Engineering, Cyber Security Center
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