A Probabilistic Learning Approach to Whole-Genome Operon Prediction
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A fuzzy guided genetic algorithm for operon prediction
Bioinformatics
Operon prediction without a training set
Bioinformatics
Improving Operon Prediction in E. coli
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Guest editorial: Integrative data mining in systems biology: from text to network mining
Artificial Intelligence in Medicine
Operon Prediction Using Chaos Embedded Particle Swarm Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Objective: The prediction of operons is critical to the reconstruction of regulatory networks at the whole genome level. Multiple genome features have been used for predicting operons. However, multiple genome features are usually dealt with using only single method in the literatures. The aim of this paper is to develop a combined method for operon prediction by using different methods to preprocess different genome features in order for exerting their unique characteristics. Methods: A novel multi-approach-guided genetic algorithm for operon prediction is presented. We exploit different methods for intergenic distance, cluster of orthologous groups (COG) gene functions, metabolic pathway and microarray expression data. A novel local-entropy-minimization method is proposed to partition intergenic distance. Our program can be used for other newly sequenced genomes by transferring the knowledge that has been obtained from Escherichia coli data. We calculate the log-likelihood for COG gene functions and Pearson correlation coefficient for microarray expression data. The genetic algorithm is used for integrating the four types of data. Results: The proposed method is examined on E. coliK12 genome, Bacillus subtilis genome, and Pseudomonas aeruginosa PAO1 genome. The accuracies of prediction for these three genomes are 85.9987%, 88.296%, and 81.2384%, respectively. Conclusion: Simulated experimental results demonstrate that in the genetic algorithm the preprocessing for genome data using multiple approaches ensures the effective utilization of different biological characteristics. Experimental results also show that the proposed method is applicable for predicting operons in prokaryote.