Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Bayesian Methods for Elucidating Genetic Regulatory Networks
IEEE Intelligent Systems
Theoretical Computer Science
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
Dynamic Algorithm for Inferring Qualitative Models of Gene Regulatory Networks
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
A First Course in Information Theory (Information Technology: Transmission, Processing and Storage)
Qualitative simulation of genetic regulatory networks: method and application
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Attempt to design a bio-medical knowledge discovery system
International Journal of Bio-Inspired Computation
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We introduce a novel algorithm, DFL (Discrete Function Learning), for reconstructing qualitative models of Gene Regulatory Networks (GRNs) from gene expression data in this paper. We analyse its complexity of O(k · N · n²) on the average and its data requirements. The experiments of synthetic Boolean networks show that the DFL algorithm is more efficient than current algorithms without loss of prediction performances. The results of yeast cell cycle gene expression data show that the DFL algorithm can identify biologically significant models with reasonable accuracy, sensitivity and high precision with respect to the literature evidences.