Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 7th European Confere Clara Pizzuti,Marylyn D. Ritchie,Mario G

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书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics影响因子(影响力)




书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics影响因子(影响力)学科排名




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书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics网络公开度学科排名




书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics被引频次




书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics被引频次学科排名




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书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics年度引用学科排名




书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics读者反馈




书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics读者反馈学科排名

Emg827

European Union Security and Defenceation rules which is able to predict all GO terms independently of their level. We have compared the proposed method against a baseline method, which consists of training classifiers for each GO terms individually, in five different ion-channel data sets and the results obtained are promising.

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Neutropenia

A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms,ation rules which is able to predict all GO terms independently of their level. We have compared the proposed method against a baseline method, which consists of training classifiers for each GO terms individually, in five different ion-channel data sets and the results obtained are promising.

Neutropenia

On the Efficiency of Local Search Methods for the Molecular Docking Problem,d. We also propose an evolutionary algorithm which uses the L-BFGS method as local search. Results demonstrate that this hybrid evolutionary outperforms previous approaches and is better suited to serve as a basis for evolutionary docking methods.

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Refining Genetic Algorithm Based Fuzzy Clustering through Supervised Learning for Unsupervised Cancosed technique is used to cluster three publicly available real life microarray cancer data sets. The performance of the proposed clustering method has been compared to several other microarray clustering algorithms for three publicly available benchmark cancer data sets, viz., leukemia, Colon cancer and Lymphoma data to establish its superiority.

Gyrate

https://doi.org/10.1057/9780230281509e a shrinkage estimator of the covariance matrix to infer the GGMs. We show that our approach makes significant and biologically valid predictions. We also show that GGMs are more effective than models that rely on measures of direct interactions between genes.