{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T13:32:30Z","timestamp":1781962350529,"version":"3.54.5"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"21","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: It is an important and difficult task to extract gene network information from high-throughput genomic data. A common approach is to cluster genes using pairwise correlation as a distance metric. However, pairwise correlation is clearly too simplistic to describe the complex relationships among real genes since co-expression relationships are often restricted to a specific set of biological conditions\/processes. In this study, we described a three-way gene interaction model that captures the dynamic nature of co-expression relationship between a gene pair through the introduction of a controller gene.<\/jats:p><jats:p>Results: We surveyed 0.4 billion possible three-way interactions among 1000 genes in a microarray dataset containing 678 human cancer samples. To test the reproducibility and statistical significance of our results, we randomly split the samples into a training set and a testing set. We found that the gene triplets with the strongest interactions (i.e. with the smallest P-values from appropriate statistical tests) in the training set also had the strongest interactions in the testing set. A distinctive pattern of three-way interaction emerged from these gene triplets: depending on the third gene being expressed or not, the remaining two genes can be either co-expressed or mutually exclusive (i.e. expression of either one of them would repress the other). Such three-way interactions can exist without apparent pairwise correlations. The identified three-way interactions may constitute candidates for further experimentation using techniques such as RNA interference, so that novel gene network or pathways could be identified.<\/jats:p><jats:p>Contact: \u00a0lzhangli@mdanderson.org<\/jats:p><jats:p>Supplementary information: \u00a0http:\/\/odin.mdacc.tmc.edu\/~zhangli\/ThreeWay<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm482","type":"journal-article","created":{"date-parts":[[2007,10,7]],"date-time":"2007-10-07T00:24:16Z","timestamp":1191716656000},"page":"2903-2909","source":"Crossref","is-referenced-by-count":42,"title":["Extracting three-way gene interactions from microarray data"],"prefix":"10.1093","volume":"23","author":[{"given":"Jiexin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 237, Houston, TX 77030-4009, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 237, Houston, TX 77030-4009, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 237, Houston, TX 77030-4009, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2007,10,5]]},"reference":[{"key":"2023041107264371900_","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/S0959-440X(03)00031-9","article-title":"Biological networks","volume":"13","author":"Alm","year":"2003","journal-title":"Curr. 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