Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Lateral Inhibition through Delta-Notch Signaling: A Piecewise Affine Hybrid Model
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
Hybrid Modeling and Simulation of Biomolecular Networks
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
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
Identifying Simple Discriminatory Gene Vectors with an Information Theory Approach
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks
International Journal of Data Mining and Bioinformatics
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It is still an open problem to identify functional relations with o(N·n^k) time for any domain [2], where N is the number of learning instances, n is the number of genes (or variables) in the Gene Regulatory Network (GRN) models and k is the indegree of the genes. To solve the problem, we introduce a novel algorithm, DFL (Discrete Function Learning), for reconstructing qualitative models of GRNs from gene expression data in this paper. We analyze its complexity of O(k · N · n^2) on the average and its data requirements. We also perform experiments on both synthetic and Cho et al. [7] yeast cell cycle gene expression data to validate the efficiency and prediction performance of the DFL algorithm. 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.We further introduce a method called 驴 function to deal with noises in data sets. The experimental results show that the 驴 function method is a good supplement to the DFL algorithm.