A multivariate prediction model for microarray cross-hybridization

BMC Bioinformatics
Yian A ChenJonas S Almeida

Abstract

Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular...Continue Reading

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Citations

May 22, 2007·Marine Biotechnology·Raphael CohenMoshe Tom
Feb 27, 2008·Nucleic Acids Research·Hinanit Koltai, Carmiya Weingarten-Baror
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Aug 2, 2007·Marine Biotechnology·Matthew J JennyGregory W Warr

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Methods Mentioned

BETA
cDNA array
PCR

Software Mentioned

ssearch34
R
MathType
Mfold

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