Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth015
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Chemical and Biological Engineering, Tufts University, Medford, MA
* To whom correspondence should be addressed. E-mail: ireis{at}sbi.org.
Motivations: Classification of biological samples for diagnostic purposes is a difficult task because of the many decisions involved on the number, type, and functional manipulations of the input variables. This study presents a generally applicable strategy for systematic formulation of optimal diagnostic indexes. To this end, we develop a novel set of computational tools by integrating regression optimization, step-wise variable selection, and cross validation algorithms. Results: The proposed discrimination methodology was applied to plasma and tissue (liver) metabolic profiling data describing the time progression of liver dysfunction in a rat model of acute hepatic failure generated by D-galactosamine (GalN) injection. From the plasma data, our methodology identified 7 (out of a total of 23) metabolites, and the corresponding transform functions, as the best inputs to the optimal diagnostic index. This index showed better time resolution and increased noise robustness compared to an existing metabolic index, Fisher's BCAA/AAA molar ratio, as well as indexes generated using other commonly used discriminant analysis tools. Comparison of plasma and liver indexes found two consensus metabolites, lactate and glucose, which implicate glycolysis and/or gluconeogenesis in mediating the metabolic effects of GalN.
Accepted September 9, 2003
Article
Identification of optimal classification functions for biological sample and state discrimination from metabolic profiling data
2 Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA
3 Shriners Burns Hospital and Center for Engineering in Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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