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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Yoshimoto, J., & Doya, K. (2008). Bayesian system identification of molecular cascades. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4984 LNCS, pp. 614–624). https://doi.org/10.1007/978-3-540-69158-7_64
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