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The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL). We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as <jats:italic>Gas1<\/jats:italic> and <jats:italic>Gli1<\/jats:italic>, along with a short list of likely targets such as <jats:italic>Mig12<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-10-433","type":"journal-article","created":{"date-parts":[[2009,12,18]],"date-time":"2009-12-18T20:43:41Z","timestamp":1261169021000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway"],"prefix":"10.1186","volume":"10","author":[{"given":"Abhik","family":"Shah","sequence":"first","affiliation":[]},{"given":"Toyoaki","family":"Tenzen","sequence":"additional","affiliation":[]},{"given":"Andrew P","family":"McMahon","sequence":"additional","affiliation":[]},{"given":"Peter J","family":"Woolf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2009,12,18]]},"reference":[{"issue":"13","key":"3163_CR1","doi-asserted-by":"publisher","first-page":"6463","DOI":"10.1158\/0008-5472.CAN-06-0462","volume":"66","author":"EJ Michaud","year":"2006","unstructured":"Michaud EJ, Yoder BK: The primary cilium in cell signaling and cancer. 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