Weighting and Feature Selection on Gene-Expression data by the use of Genetic Algorithms

Authors:
Olga M. Pérez;Manuel Hidalgo-Conde;Francisco J. Marín;Oswaldo Trelles
Affiliations:
Computer Architecture Department, University of Malaga, Malaga, Spain 29080;Computer Architecture Department, University of Malaga, Malaga, Spain 29080;Electronic Department, University of Malaga, Malaga, Spain 29080;Computer Architecture Department, University of Malaga, Malaga, Spain 29080 and Integromics, Parque Científico de Madrid, Madrid 28049
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

One of the most promising approaches for gaining insight into the biological activity of genes is to study their expression patterns in a variety of experimental conditions and contexts. In this work we present a genetic- algorithm-based approach for optimizing weighting schemes of variables used to improve clustering solutions. The same technique is used for feature selection and the detection of marker components in large datasets. An original string representation based on real numbers is used to encode the variable weight, and a modified silhouette value is used as fitness function. The strategy has a generic and parametric formulation, and effectiveness is demonstrated on gene-expression data.