A Patient-Gene Model for Temporal Expression Profiles in Clinical Studies
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.
KeywordsGene Ontology Multiple Sclerosis Patient Common Response Yeast Cell Cycle Subharmonic Solution
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- 1.Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisia by microarray hybridization. Mol. Biol. of the Cell. 9, 3273–3297 (1998)Google Scholar
- 3.Inflammation and the Host Response to Injury, www.gluegrant.org
- 6.Weinstock-Guttman, B., Badgett, D., Patrick, K., Hartrich, L., et al.: Genomic effects of IFN-beta in multiple sclerosis patients. J. Immunol. 171(5), 1503–1508 (2002)Google Scholar
- 9.Bar-Joseph, Z., Gerber, G., Jaakkola, T.S., Gifford, D.K., Simon, I.: Continuous Representations of Time Series Gene Expression Data. Journal of Computational Biology 3-4, 39–48 (2003)Google Scholar
- 11.Gaffney, S., Smyth, P.: Joint Probabilistic Curve Clustering and Alignmen. In: Proceedings of The Eighteenth Annual Conference on Neural Information Processing Systems (NIPS) (2004)Google Scholar
- 15.Tarantello, G.: Subharmonic solutions for Hamiltonian systems via a ZZ p pseudoindex theory. Annali di Matematica Pura (to appear)Google Scholar
- 17.Sharan, R., Shamir, R.: Algorithmic Approaches to Clustering Gene Expression Data. Current Topics in Computational Biology, 269–300 (2002)Google Scholar
- 18.Piegl, L., Tiller, W.: The NURBS Book. Springer, New York (1997)Google Scholar
- 20.Xing, E.P., Jordan, M.I., Russell, S.: A generalized mean field algorithm for variational inference in exponential families. In: Proceedings of Uncertainty in Artificial Intelligence (UAI), pp. 583–591 (2003)Google Scholar
- 21.Supporting website: www.cs.cmu.edu/~zivbj/comb/combpatient.html
- 23.Takeba, Y., et al.: Txk, a member of nonreceptor tyrosine kinase of Tec family, acts as a Th1 cell-specific transcription factor and regulates IFN-gamma gene transcription. J. Immunol. 168(5), 2365–2370 (2002)Google Scholar