A multivariate prediction model for microarray cross-hybridization

Background: 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. Results: 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 pr...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Yian A. Chen, Cheng-Chung Chou, Xinghua Lu, Elizabeth H. Slate, Konan Peck, Wenying Xu, Eberhard O. Voit, Jonas S. Almeida
• Keywords: BMCBI 2006, Expression Microarrays, Multiple Multivariate Models, Multivariate