IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Gene association/interaction networks provide vast amounts of information about essential processes inside the cell. A complete picture of gene–gene associations/interactions would open new horizons for biologists, ranging from pure appreciation to successful manipulation of biological pathways for therapeutic purposes. Therefore, identification of important biological complexes whose members (genes and their products proteins) interact with each other is of prime importance. Numerous experimental methods exist but, for the most part, they are costly and labor intensive. Computational techniques, such as the one proposed in this work, provide a quick ‘budget’ solution that can be used as a screening tool before more expensive techniques are attempted. Here, we introduce a novel computational method based on the partial least squares (PLS) regression technique for reconstruction of genetic networks from microarray data. Results: The proposed PLS method is shown to be an effective screening procedure for the detection of gene–gene interactions from microarray data. Both simulated and real microarray experiments show that the PLS-based approach is superior to its competitors both in terms of performance and applicability. Availability: R code is available from the supplementary web-site whose URL is given below. Contact: susmita.datta@louisville.edu Supplementary information: Supplementary information are available at http://www.susmitadatta.org/Supp/GeneNet/supp.htm.