{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:03:47Z","timestamp":1760547827687},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2004,8,10]],"date-time":"2004-08-10T00:00:00Z","timestamp":1092096000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"},{"start":{"date-parts":[[2004,8,10]],"date-time":"2004-08-10T00:00:00Z","timestamp":1092096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusion<\/jats:title>\n                        <jats:p>Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput \"reverse engineering\" of complex biological systems.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-5-108","type":"journal-article","created":{"date-parts":[[2004,8,14]],"date-time":"2004-08-14T06:22:48Z","timestamp":1092464568000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Linear fuzzy gene network models obtained from microarray data by exhaustive search"],"prefix":"10.1186","volume":"5","author":[{"given":"Bahrad A","family":"Sokhansanj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick J","family":"Fitch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Judy N","family":"Quong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew A","family":"Quong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2004,8,10]]},"reference":[{"key":"224_CR1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1093\/nar\/30.1.56","volume":"30","author":"PD Karp","year":"2002","unstructured":"Karp PD, Riley M, Saier M, Paulsen IT, Paley S, Pellegrini-Toole A: The Ecocyc database.\n                           Nucleic Acids Res 2002, 30: 56. 10.1093\/nar\/30.1.56","journal-title":"Nucleic Acids Res"},{"key":"224_CR2","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1016\/S0969-2126(02)00861-4","volume":"10","author":"AE Todd","year":"2002","unstructured":"Todd AE, Orengo CA, Thornton JM: Sequence and structural differences between enzyme and nonenzyme homologs.\n                           Structure (Camb.) 2002, 10: 1435\u20131451. 10.1016\/S0969-2126(02)00861-4","journal-title":"Structure (Camb.)"},{"key":"224_CR3","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/5.899061","volume":"88","author":"JP Fitch","year":"2000","unstructured":"Fitch JP, Sokhansanj B: Genomic engineering: moving beyond DNA sequence to function.\n                           Proc IEEE 2000, 88: 1949\u20131971. 10.1109\/5.899061","journal-title":"Proc IEEE"},{"key":"224_CR4","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1074\/mcp.M200001-MCP200","volume":"1","author":"TJ Griffin","year":"2002","unstructured":"Griffin TJ, Gygi SP, Ideker T, Rist B, Eng J, Hood L, Aebersold R: Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae.\n                           Mol Cell Proteomics 2002, 1: 323\u2013333. 10.1074\/mcp.M200001-MCP200","journal-title":"Mol Cell Proteomics"},{"key":"224_CR5","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1002\/mas.10040","volume":"21","author":"CE Forde","year":"2002","unstructured":"Forde CE, McCutchen-Maloney SL: Characterization of transcription factors by mass spectrometry and the role of SELDI-MS.\n                           Mass Spectrom Rev 2002, 21: 419\u2013439. 10.1002\/mas.10040","journal-title":"Mass Spectrom Rev"},{"key":"224_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/pmic.200390007","volume":"3","author":"PL Cutler","year":"2003","unstructured":"Cutler PL: Protein arrays: the current state-of-the-art.\n                           Prteomics 2003, 3: 3\u201318. 10.1002\/pmic.200390007","journal-title":"Prteomics"},{"key":"224_CR7","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1101\/gad.1060703","volume":"17","author":"J Pothof","year":"2003","unstructured":"Pothof J, van Haaften G, Thijssen K, Kamath RS, Fraser AG, Ahringer J, Plasterk RH, Tijsterman M: Identification of genes that protect the C. elegans genome against mutations by genome-wide RNAi.\n                           Genes Dev 2003, 17: 443\u2013448. 10.1101\/gad.1060703","journal-title":"Genes Dev"},{"key":"224_CR8","first-page":"18","volume":"3","author":"S Liang","year":"2000","unstructured":"Liang S, Fuhrman S, Somogyi R: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures.\n                           Pacific Symposium on Biocomputing 2000, 3: 18\u201329. 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