{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T09:46:28Z","timestamp":1773308788520,"version":"3.50.1"},"reference-count":10,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2004,7,23]],"date-time":"2004-07-23T00:00:00Z","timestamp":1090540800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"},{"start":{"date-parts":[[2004,7,23]],"date-time":"2004-07-23T00:00:00Z","timestamp":1090540800000},"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>One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusions<\/jats:title>\n                        <jats:p>GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-5-100","type":"journal-article","created":{"date-parts":[[2004,7,27]],"date-time":"2004-07-27T06:23:22Z","timestamp":1090909402000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Graph-based iterative Group Analysis enhances microarray interpretation"],"prefix":"10.1186","volume":"5","author":[{"given":"Rainer","family":"Breitling","sequence":"first","affiliation":[]},{"given":"Anna","family":"Amtmann","sequence":"additional","affiliation":[]},{"given":"Pawel","family":"Herzyk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2004,7,23]]},"reference":[{"key":"216_CR1","first-page":"111","volume":"12","author":"S Dudoit","year":"2002","unstructured":"Dudoit S, Yang YH, Callow MJ, Speed TP: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments.\n                           Statistica Sinica 2002, 12: 111\u2013139.","journal-title":"Statistica Sinica"},{"key":"216_CR2","unstructured":"Breitling R, Armengaud P, Amtmann A, Herzyk P: Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.\n                           FEBS Letters, in press."},{"key":"216_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/1471-2105-5-34","volume":"5","author":"R Breitling","year":"2004","unstructured":"Breitling R, Amtmann A, Herzyk P: Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments.\n                           BMC Bioinformatics 2004, 5: 34. 10.1186\/1471-2105-5-34","journal-title":"BMC Bioinformatics"},{"key":"216_CR4","doi-asserted-by":"publisher","first-page":"R7","DOI":"10.1186\/gb-2003-4-1-r7","volume":"4","author":"SW Doniger","year":"2003","unstructured":"Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data.\n                           Genome Biol 2003, 4: R7. 10.1186\/gb-2003-4-1-r7","journal-title":"Genome Biol"},{"key":"216_CR5","doi-asserted-by":"publisher","first-page":"R70","DOI":"10.1186\/gb-2003-4-10-r70","volume":"4","author":"DA Hosack","year":"2003","unstructured":"Hosack DA, Dennis G., Jr., Sherman BT, Lane HC, Lempicki RA: Identifying biological themes within lists of genes with EASE.\n                           Genome Biol 2003, 4: R70. 10.1186\/gb-2003-4-10-r70","journal-title":"Genome Biol"},{"key":"216_CR6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/1471-2105-4-12","volume":"4","author":"CC Kim","year":"2003","unstructured":"Kim CC, Falkow S: Significance analysis of lexical bias in microarray data.\n                           BMC Bioinformatics 2003, 4: 12. 10.1186\/1471-2105-4-12","journal-title":"BMC Bioinformatics"},{"key":"216_CR7","first-page":"271","volume":"2003","author":"NJ Provart","year":"2003","unstructured":"Provart NJ, Zhu T: A Browser-based Functional Classification SuperViewer for Arabidopsis Genomics.\n                           Currents in Computational Molecular Biology 2003, 2003: 271\u2013272.","journal-title":"Currents in Computational Molecular Biology"},{"key":"216_CR8","doi-asserted-by":"publisher","first-page":"R28","DOI":"10.1186\/gb-2003-4-4-r28","volume":"4","author":"BR Zeeberg","year":"2003","unstructured":"Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN: GoMiner: a resource for biological interpretation of genomic and proteomic data.\n                           Genome Biol 2003, 4: R28. 10.1186\/gb-2003-4-4-r28","journal-title":"Genome Biol"},{"key":"216_CR9","doi-asserted-by":"publisher","first-page":"S233","DOI":"10.1093\/bioinformatics\/18.suppl_1.S233","volume":"18","author":"T Ideker","year":"2002","unstructured":"Ideker T, Ozier O, Schwikowski B, Siegel AF: Discovering regulatory and signalling circuits in molecular interaction networks.\n                           Bioinformatics 2002, 18: S233-S240.","journal-title":"Bioinformatics"},{"key":"216_CR10","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1126\/science.278.5338.680","volume":"278","author":"JL DeRisi","year":"1997","unstructured":"DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale.\n                           Science 1997, 278: 680\u2013686. 10.1126\/science.278.5338.680","journal-title":"Science"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-5-100.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/1471-2105-5-100\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-5-100.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T12:16:01Z","timestamp":1728303361000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-5-100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,7,23]]},"references-count":10,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2004,12]]}},"alternative-id":["216"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-5-100","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004,7,23]]},"assertion":[{"value":"27 April 2004","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2004","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2004","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"100"}}