Pattern Recognition for Small-Sample Genomic Data

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Modern biomedical applications based on genomic and proteomic expression profiles often produce large amounts of data characterized by a very large number of measurements made on a much smaller number of sample points. This introduces difficult challenges in the application of classification methods to obtain accurate predictive models. In this talk, we discuss our recent work on this topic, highlighting Bayesian approaches to classification of proteomic and metagenomic data, as well as novel approaches for classification and error estimation based on restricted and nonstationary data.


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  • Date: 22 Feb 2018
  • Time: 06:00 PM to 08:30 PM
  • All times are (GMT-06:00) US/Central
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  • 5430 Westheimer Rd
  • Houston, Texas
  • United States TX 77056
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  • Co-sponsored by Ernest Njouondo
  • Starts 28 January 2018 11:14 AM
  • Ends 22 February 2018 01:00 AM
  • All times are (GMT-06:00) US/Central
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  Speakers

Ulisses M. Braga-Neto Ulisses M. Braga-Neto of TXAMU, Center for Bioinformatics and Genomic Systems

Topic:

Pattern Recognition for Small-Sample Genomic Data

Biography:

Ulisses M. Braga-Neto is an Associate Professor at the Department of Electrical and Computer Engineering and a member of the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, College Station, Texas, USA. He received a B.S. degree from the Universidade Federal de Pernambuco (UFPE), Recife, Brazil, in 1992, an M.Sc. degree from Universidade de Campinas (UNICAMP), Campinas, Brazil, in 1994, and a Ph.D. degree from The Johns Hopkins University, Baltimore, MD, USA in 2002, all in Electrical Engineering. He held post-doctoral positions at the University of Texas M.D. Anderson Cancer Center, Houston, TX, USA, from 2002-2004 and at the Oswaldo Cruz Foundation, Recife, Brazil. His research interests include Pattern Recognition and Statistical Signal Processing with applications in Genomics and Materials Science. He is the co-author of the textbook "Error Estimation for Pattern Recognition" (IEEE-Wiley, 2015) and has received the NSF CAREER Award for his work in that area.

 He is a member of the Machine Learning for Signal Processing TC and the Bio Imaging and Signal Processing TC, both of the IEEE Signal Processing Society. (TC = Technical Committee).

 

Address:Bryan College Station, Texas, United States




Agenda

6:30 Registration

6:45 Dinner

7:10 Presentation

8:10 Q&A

8:15 Closing Remarks