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Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4612-0919-5_38openalex-snapshot
Information Theory and an Extension of the Maximum Likelihood Principle
inferredcites →https://doi.org/10.1007/978-1-4612-1694-0_15openalex-snapshot
Information Theory and an Extension of the Maximum Likelihood Principle
inferredcites →https://doi.org/10.1007/bf00047572openalex-snapshot
Simulated annealing: Theory and applications
inferredcites →https://doi.org/10.1016/s0925-2312(01)00330-7openalex-snapshot
Causality: Models, Reasoning, and Inference
inferredcites →https://doi.org/10.1023/a:1022602019183openalex-snapshot
Genetic Algorithms and Machine Learning
inferredcites →https://doi.org/10.1038/msb4100120openalex-snapshot
How to infer gene networks from expression profiles
inferredcites →https://doi.org/10.1038/nmeth.3773openalex-snapshot
Inferring causal molecular networks: empirical assessment through a community-based effort
inferredcites →https://doi.org/10.1038/nprot.2008.179openalex-snapshot
Reverse-phase protein lysate microarrays for cell signaling analysis
inferredcites →https://doi.org/10.1089/106652700750050961openalex-snapshot
Using Bayesian Networks to Analyze Expression Data
inferredcites →https://doi.org/10.1093/bioinformatics/bth463openalex-snapshot
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data
inferredcites →https://doi.org/10.1142/9789812799623_0017openalex-snapshot
ESTIMATION OF GENETIC NETWORKS AND FUNCTIONAL STRUCTURES BETWEEN GENES BY USING BAYESIAN NETWORKS AND NONPARAMETRIC REGRESSION
inferredcites →https://doi.org/10.1142/9789814447300_0003openalex-snapshot
IDENTIFICATION OF GENETIC NETWORKS FROM A SMALL NUMBER OF GENE EXPRESSION PATTERNS UNDER THE BOOLEAN NETWORK MODEL
inferredcites →https://doi.org/10.1145/168304.168305openalex-snapshot
Genetic algorithms and machine learning
inferredcites →https://doi.org/10.1186/1471-2105-11-1openalex-snapshot
Detection of gene pathways with predictive power for breast cancer prognosis
inferredcites →https://doi.org/10.1186/1471-2105-11-s1-s56openalex-snapshot
Reverse engineering gene regulatory network from microarray data using linear time-variant model
inferredcites →https://doi.org/10.1186/1752-0509-4-162openalex-snapshot
Regulation patterns in signaling networks of cancer
inferredcites →https://doi.org/10.1196/annals.1407.021openalex-snapshot
Dialogue on Reverse‐Engineering Assessment and Methods
inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot
Estimating the Dimension of a Model
inferredcites →https://doi.org/10.18637/jss.v035.i03openalex-snapshot
Learning Bayesian Networks with the bnlearn R Package
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