An Evolutionary Approach for Sample-Based Clustering on Microarray Data

Authors:
Daniel Glez-Peña;Fernando Díaz;José R. Méndez;Juan M. Corchado;Florentino Fdez-Riverola
Affiliations:
ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Ourense, Spain 32004;Dept. Informática, University of Valladolid, Escuela Universitaria de Informática, Segovia, Spain 40005;ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Ourense, Spain 32004;Dept. Informática y Automática, University of Salamanca, Salamanca, Spain 37008;ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Ourense, Spain 32004
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

Citing 4
Cited 0

Class discovery in gene expression data

RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Analysis of gene expression profiles: class discovery and leaf ordering

Proceedings of the sixth annual international conference on Computational biology
Cluster Analysis for Gene Expression Data: A Survey

IEEE Transactions on Knowledge and Data Engineering
ESPD: a pattern detection model underlying gene expression profiles

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coherent from a genetic point of view. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Noise genes are identified and removed from the fuzzy pattern based on their probability of appearance. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema. Experimental results over publicly available microarray data show the effectiveness of the proposed method.