Incorporating biological knowledge into evaluation of causal regulatory hypotheses. Export

@article{citeulike:452861, abstract = {Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.}, address = {Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USA. lonnie@apres.stanford.edu}, author = {Chrisman, L. and Langley, P. and Bay, S. and Pohorille, A.}, citeulike-article-id = {452861}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12603023}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12603023}, journal = {Pac Symp Biocomput}, keywords = {bayesian, causality, dataintegration, evaluationstudies, inference, priorknowledge}, pages = {128--139}, posted-at = {2005-12-29 16:36:44}, priority = {0}, title = {Incorporating biological knowledge into evaluation of causal regulatory hypotheses.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12603023}, year = {2003} }

TY - JOUR ID - citeulike:452861 L3 - citeulike-article-id:452861 N2 - Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible. JF - Pac Symp Biocomput AD - Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USA. lonnie@apres.stanford.edu EP - 139 TI - Incorporating biological knowledge into evaluation of causal regulatory hypotheses. SP - 128 KW - bayesian KW - causality KW - dataintegration KW - evaluationstudies KW - inference KW - priorknowledge AU - Chrisman, L AU - Langley, P AU - Bay, S AU - Pohorille, A PY - 2003/// UR - http://view.ncbi.nlm.nih.gov/pubmed/12603023 ER -