The NURBS book
Analyzing time series gene expression data
Bioinformatics
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Extracting Dynamics from Static Cancer Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
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Pharmacogenomics and clinical studies that measure the temporal expression levels of patients can identify important pathways and biomarkers that are activated during disease progression or in response to treatment. However, researchers face a number of challenges when trying to combine expression profiles from these patients. Unlike studies that rely on lab animals or cell lines, individuals vary in their baseline expression and in their response rate. In this paper we present a generative model for such data. Our model represents patient expression data using two levels, a gene level which corresponds to a common response pattern and a patient level which accounts for the patient specific expression patterns and response rate. Using an EM algorithm we infer the parameters of the model. We used our algorithm to analyze multiple sclerosis patient response to Interferon-β. As we show, our algorithm was able to improve upon prior methods for combining patients data. In addition, our algorithm was able to correctly identify patient specific response patterns.