A hierarchical unsupervised growing neural network for clustering gene expression patterns | Zendy

Javier Herrero | Zendy; Alfonso Valencia | Zendy; Joaquı́n Dopazo | Zendy

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A hierarchical unsupervised growing neural network for clustering gene expression patterns

Author(s) -

Javier Herrero,

Alfonso Valencia,

Joaquı́n Dopazo

Publication year - 2001

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/17.2.126

Subject(s) - cluster analysis , computer science , artificial neural network , artificial intelligence , hierarchical clustering , computational biology , pattern recognition (psychology) , data mining , machine learning , biology

We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226-233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network.

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