Biomedical subjects

Pat Langley

Publications and source records attributed to Pat Langley.

3 recordsLinked to original sources

PubMed2006

Constructing explanatory process models from biological data and knowledge.

OBJECTIVE: We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS: We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. RESULTS: We demonstrate IPM's use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. CONCLUSION: We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

Algorithms↗

PubMed2003

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.

Algorithms↗

PubMed2002

Guiding revision of regulatory models with expression data.

BioLingua is a computational system designed to support biologists' efforts to construct models, make predictions, and interpret data. In this paper, we focus on the specific task of revising an initial model of gene regulation based on expression levels from gene microarrays. We describe BioLingua's formalism for representing process models, its method for predicting qualitative correlations from such models, and its use of data to constrain search through the space of revised models. We also report experimental results on revising a model of photosynthetic regulation in Cyanobacteria to better fit expression data for both wild and mutant strains, along with model mutilation studies designed to test our method's robustness. In closing, we discuss related work on representing, discovering, and revising biological models, after which we propose some directions for future research.

Computational Biology↗