Conference Paper

An Evolutionary Approach for Sample-Based Clustering on Microarray Data.

DOI: 10.1007/978-3-642-02481-8_148 Conference: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II
Source: DBLP

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.

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    Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II; 01/2009

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