{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:57:49Z","timestamp":1761058669294},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Syst Biol"],"published-print":{"date-parts":[[2007,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>The Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the <jats:italic>in situ<\/jats:italic> hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In order to address the pattern comparison challenge when analyzing the ABA database, we developed a robust image filtering method, dubbed histogram-row-column (HRC) algorithm. We demonstrated how the HRC algorithm offers the sensitivity of identifying a manageable number of gene pairs based on automatic pattern searching from an original large brain image collection. This tool enables us to quickly identify genes of similar <jats:italic>in situ<\/jats:italic> hybridization patterns in a semi-automatic fashion and consequently allows us to discover several gene expression patterns with expression neighborhoods containing genes of similar functional categories.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Given a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection. A three-dimensional <jats:italic>in situ<\/jats:italic> hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function. Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-1-19","type":"journal-article","created":{"date-parts":[[2007,4,30]],"date-time":"2007-04-30T15:53:01Z","timestamp":1177948381000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Study of gene function based on spatial co-expression in a high-resolution mouse brain atlas"],"prefix":"10.1186","volume":"1","author":[{"given":"Zheng","family":"Liu","sequence":"first","affiliation":[]},{"given":"S Frank","family":"Yan","sequence":"additional","affiliation":[]},{"given":"John R","family":"Walker","sequence":"additional","affiliation":[]},{"given":"Theresa A","family":"Zwingman","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yingyao","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2007,4,16]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"e24","DOI":"10.1371\/journal.pbio.0030024","volume":"3","author":"V Gewin","year":"2005","unstructured":"Gewin V: A golden age of brain exploration. 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