Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.

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Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

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2016年論文

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Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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P1476

Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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P2093

Guodong Zhao

http://www.w3.org/2001/XMLSchema#string

Yan Wu

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P2860

Finding and evaluating community structure in networks

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Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12.

Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method.

Selection bias in gene extraction on the basis of microarray gene-expression data.

Gene selection from microarray data for cancer classification--a machine learning approach.

Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.

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srep34759

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scholarly article

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10.1038/SREP34759

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English

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2016-10-05T00:00:00Z

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Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

about

P2860

P2860

Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

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type

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P1476

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