A Patient-Gene Model for Temporal Expression Profiles in Clinical Studies

  • Naftali Kaminski
  • Ziv Bar-Joseph
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)


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.


Gene Ontology Multiple Sclerosis Patient Common Response Yeast Cell Cycle Subharmonic Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Naftali Kaminski
    • 1
  • Ziv Bar-Joseph
    • 2
  1. 1.Simmons Center for Interstitial Lung DiseaseUniversity of Pittsburgh Medical SchoolPittsburghUSA
  2. 2.School of Computer Science and Department of BiologyCarnegie Mellon UniversityPittsburghUSA

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