TY - GEN
T1 - Bayesian system identification of molecular cascades
AU - Yoshimoto, Junichiro
AU - Doya, Kenji
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - Biochemical kinetics
KW - Markov chain Monte Carlo method
KW - System identification
KW - Systems biology
UR - https://www.scopus.com/pages/publications/54249115964
UR - https://www.scopus.com/pages/publications/54249115964#tab=citedBy
U2 - 10.1007/978-3-540-69158-7_64
DO - 10.1007/978-3-540-69158-7_64
M3 - Conference contribution
AN - SCOPUS:54249115964
SN - 3540691545
SN - 9783540691549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 614
EP - 624
BT - Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
T2 - 14th International Conference on Neural Information Processing, ICONIP 2007
Y2 - 13 November 2007 through 16 November 2007
ER -