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Process pathway inference via time series analysis

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Abstract

Motivated by recent experimental developments in functional genomics, we construct and test a numerical technique for inferring process pathways, in which one process calls another process, from time series data. We validate using a case in which data are readily available and we formulate an extension, appropriate for genetic regulatory networks, which exploits Bayesian inference and in which the present-day undersampling is compensated for by prior understanding of genetic regulation.

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Authors and Affiliations

  1. Department of Applied Physics and Applied Mathematics, Columbia University, 10027, NY

    C. H. Wiggins (Assistant Professor)

  2. Center for Computational Biology and Bioinformatics (C2B2), Columbia University, 10027, NY

    C. H. Wiggins (Assistant Professor)

  3. Kavli Institute for Theoretical Physics, University of California, 93106, Santa Barbara, CA

    C. H. Wiggins (Assistant Professor) & I. Nemenman (Post-Doctoral Research Scientist)

Authors
  1. C. H. Wiggins
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  2. I. Nemenman
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Wiggins, C.H., Nemenman, I. Process pathway inference via time series analysis. Experimental Mechanics 43, 361–370 (2003). https://doi.org/10.1007/BF02410536

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  • DOI: https://doi.org/10.1007/BF02410536

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