Title :
Bayesian methods for elucidating genetic regulatory networks
Author :
Hartemink, Alexander J. ; Gifford, David K. ; Jaakkola, Tommi S. ; Young, Richard A.
Author_Institution :
Duke Univ., Durham, NC, USA
Abstract :
Bayesian network methods are useful for elucidating genetic regulatory networks because they can represent more than pair-wise relationships between variables, are resistant to overfitting, and remain robust in the face of noisy data.
Keywords :
belief networks; biocontrol; biology computing; genetics; proteins; Bayesian networks; gene expression; genetic regulatory network elucidation; noisy data robustness; overfitting resistance; proteins; variable relationships; Bayesian methods; Bioinformatics; Biological system modeling; Data analysis; Data mining; Gene expression; Genetics; Genomics; Pattern analysis; Proteins;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2002.999218
