Incorporating biological knowledge into evaluation of causal regulatory hypotheses.
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.
Chrisman
Lonnie
L
Institute for the Study of Learning and Expertise, 2164 Staunton Court, Palo Alto, CA 94306, USA. lonnie@apres.stanford.edu
Langley
Pat
P
Bay
Stephen
S
Pohorille
Andrew
A
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology; D006801:Humans; D020935:MAP Kinase Signaling System; D051379:Mice; D048055:Mitogen-Activated Protein Kinase 8; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D008954:Models, Biological; D015233:Models, Statistical; D016755:Proto-Oncogene Proteins c-jun / Q000378:metabolism
D016755:Proto-Oncogene Proteins c-jun; D048055:Mitogen-Activated Protein Kinase 8; D020928:Mitogen-Activated Protein Kinases