Regression and classification for metabolite profile analysis

3^rd International Conference and Exhibition on Metabolomics & Systems Biology

March 24-26, 2014 Hilton San Antonio Airport, San Antonio, USA

Kelsi Perttula, K. Jarrod Millman and Sara Moore

Accepted Abstracts: Metabolomics

Abstract :

Recent advances in metabolite (and lipid) profiling can link genetics, exposures, and risks of chronic diseases. Here, several methods for identifying biomarkers are applied to data from a metabolomic study of healthy volunteers. The primary objective of this work is to compare the performance of these methods and to determine which best differentiatethe known groups in the sample, with the ultimate goal of applying the method(s) with the best performance to future clinical samples.

Biography :

Kelsi Perttula is a second year Ph.D. student in Stephen Rappaport?s laboratory at UC Berkeley. She previously earned a BS in chemistry at UC Berkeley, and an MS in Chemistry at San Jose State University. After a ten year career as a forensic chemist and DNA analyst, in 2012 she joined the Environmental Health Science Division of UC Berkeley?s School of Public Health.