Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Elements of information theory
Elements of information theory
The upward bias in measures of information derived from limited data samples
Neural Computation
Data Mining and Knowledge Discovery
Signal Processing - Special issue: Genomic signal processing
Estimation of entropy and mutual information
Neural Computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Estimating entropy on m bins given fewer than m samples
IEEE Transactions on Information Theory
Fast calculation of pairwise mutual information for gene regulatory network reconstruction
Computer Methods and Programs in Biomedicine
Analysis of the GRNs Inference by Using Tsallis Entropy and a Feature Selection Approach
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
SFFS-MR: a floating search strategy for GRNs inference
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Inference of restricted stochastic boolean GRN's by Bayesian error and entropy based criteria
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Application of logic synthesis to the understanding and cure of genetic diseases
Proceedings of the 49th Annual Design Automation Conference
Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A pattern-oriented specification of gene network inference processes
Computers in Biology and Medicine
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Recently, the concept of mutual information has been proposed for infering the structure of genetic regulatory networks from gene expression profiling. After analyzing the limitations of mutual information in inferring the gene-to-gene interactions, this paper introduces the concept of conditional mutual information and based on it proposes two novel algorithms to infer the connectivity structure of genetic regulatory networks. One of the proposed algorithms exhibits a better accuracy while the other algorithm excels in simplicity and flexibility. By exploiting the mutual information and conditional mutual information, a practical metric is also proposed to assess the likeliness of direct connectivity between genes. This novel metric resolves a common limitation associated with the current inference algorithms, namely the situations where the gene connectivity is established in terms of the dichotomy of being either connected or disconnected. Based on the data sets generated by synthetic networks, the performance of the proposed algorithms is compared favorably relative to existing state-of-the-art schemes. The proposed algorithms are also applied on realistic biological measurements, such as the cutaneous melanoma data set, and biological meaningful results are inferred.