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This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>\n              <jats:italic>S. cerevisiae<\/jats:italic> cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug\u2019s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions \u2013 exposure time and concentration and (ii) Network training conditions \u2013 training compendium modifications. Two analyses of SSEM-Lasso output \u2013 gene set and single gene \u2013 were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-8-7","type":"journal-article","created":{"date-parts":[[2014,1,20]],"date-time":"2014-01-20T16:23:06Z","timestamp":1390234986000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets"],"prefix":"10.1186","volume":"8","author":[{"given":"Lisa M","family":"Christadore","sequence":"first","affiliation":[]},{"given":"Lisa","family":"Pham","sequence":"additional","affiliation":[]},{"given":"Eric D","family":"Kolaczyk","sequence":"additional","affiliation":[]},{"given":"Scott E","family":"Schaus","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,1,20]]},"reference":[{"issue":"1 Suppl","key":"1276_CR1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/4475","volume":"21","author":"C Debouck","year":"1999","unstructured":"Debouck C, Goodfellow PN: DNA microarrays in drug discovery and development. 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