Chrisman, L., Langley, P., Bay, S., and Pohorille, A., "Incorporating biological knowledge into evaluation of causal regulatory hypotheses", Pacific Symposium on Biocomputing (PSB), Jan 2003.
@InProceedings{chrismanPsb2003,
author = "L. Chrisman and P. Langley and S. Bay and A. Pohorille",
title = "Incorporating Biological Knowledge Into Evaluation Of Causal Regulatory Hypotheses",
booktitle = "Pacific Symposium on Biocomputing (PSB)",
month = "Jan",
year = 2003}
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 substantially increased 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 could not be made.
Causal network, Bayesian, causal hypothesis evaluation, signal transduction cascade, JNK, c-Jun, MAPK pathway, Markov Chain Monte Carlo, MCMC, Hastings, prior knowledge.