Bayesian system identification of molecular cascades

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a Bayesian method for the system identification of molecular cascades in biological systems. The contribution of this study is to provide a theoretical framework for unifying three issues: 1) estimating the most likely parameters; 2) evaluating and visualizing the confidence of the estimated parameters; and 3) selecting the most likely structure of the molecular cascades from two or more alternatives. The usefulness of our method is demonstrated in several benchmark tests.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Number of pages11
EditionPART 1
Publication statusPublished - 2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13-11-200716-11-2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4984 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Neural Information Processing, ICONIP 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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