C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Inference of a gene regulatory network by means of interactive evolutionary computing
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Evolutionary modeling and inference of gene network
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Physical network models and multi-source data integration
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolving genetic regulatory networks using an artificial genome
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Inferring genetic regulatory logic from expression data
Bioinformatics
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
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There is a need to design computational methods to support the prediction of gene regulatory networks. Such models should offer both biologically-meaningful and computationally-accurate predictions, which in combination with other techniques may improve large-scale, integrative studies. This paper presents a new machine learning method for the prediction of putative regulatory associations from expression data, which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression dataset. The results were statistically validated and compared with the relationships inferred by two machine learning approaches to gene regulatory network prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory, large-scale studies of automated identification of potentially-relevant gene expression associations.