Analysis of Cancer Microarray Data using Constructive Neural Networks and Genetic Algorithms

被引：0

作者：

Luque-Baena, R. M. ^{[1
]}

Urda, D. ^{[1
]}

Subirats, J. L. ^{[1
]}

Franco, L. ^{[1
]}

Jerez, J. M. ^{[1
]}

机构：

[1] Univ Malaga, Dept Comp Sci, E-29071 Malaga, Spain

来源：

PROCEEDINGS IWBBIO 2013: INTERNATIONAL WORK-CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING | 2013年

关键词：

Microarray; Genetic algorithms; Constructive neural networks; FEATURE-SELECTION; MUTUAL INFORMATION; EXPRESSION; CLASSIFICATION; PREDICTION;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The analysis of microarray data typically involves a feature selection method in order to select the most relevant genes while at the same time maximizing the information content. This work presents a methodology that use the Welch t-test to filter the number of initial features embedded in two different frameworks to select the predictor genetic profile: genetic algorithm and stepwise forward selection approaches. The genetic algorithm strategy combines mutual information and classification models to predict cancer outcome. Furthermore, a constructive neural network model, C-Mantec, is applied providing reduced network architectures with competitive results in comparison to other classifiers. Six free-public cancer databases are used to test our approach.

引用

页码：55 / 63

页数：9

共 23 条

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