Joint genomic and metabolomic analysis of toxic dose-response experiments

Author

Jahns, Gary L. ; DelRaso, Nicholas ; Westrick, Mark P. ; Chan, Victor ; Reo, Nicholas V. ; Zacharewski, Timothy R.

Author_Institution

BAE Syst. Adv. Inf. Technol., San Diego, CA, USA

fYear

2005

fDate

8-11 Aug. 2005

Firstpage

195

Lastpage

196

Abstract

A methodology has been implemented for analyzing microarray and NMR spectral data obtained from the same set of toxic-exposure dose-response experiments. The NMR spectra additionally track the time course of exposure. Analyses consist of screening the data to eliminate variates with insignificant signal, normalization appropriate to the experimental design, principal components analysis, and nonlinear classification using a support vector machine. It is found that exposure at subtoxic levels can be detected.

Keywords

biological NMR; biology computing; genetics; learning (artificial intelligence); molecular biophysics; principal component analysis; support vector machines; toxicology; NMR spectral data; genomic analysis; metabolomic analysis; microarray analysis; nonlinear classification; principal components analysis; support vector machine; toxic dose-response; Bioinformatics; Genomics; Metabolomics; Nuclear magnetic resonance; Performance analysis; Principal component analysis; Probes; Signal analysis; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE

Print_ISBN

0-7695-2442-7

Type

conf

DOI

10.1109/CSBW.2005.81

Filename

1540596