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BMC Bioinformatics

, 7:101

First Online: 01 March 2006Received: 12 September 2005Accepted: 01 March 2006


BackgroundExpression 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.

ResultsWe 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 target GC content, when regression trees or ANNs are used.

ConclusionA systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-7-101 contains supplementary material, which is available to authorized users.

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Autor: Yian A Chen - Cheng-Chung Chou - Xinghua Lu - Elizabeth H Slate - Konan Peck - Wenying Xu - Eberhard O Voit - Jonas S 

Fuente: https://link.springer.com/

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