ABSTRACT Motivation: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. Results: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering genemicroarray data. Themethod,which provides auser-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. Availability: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. Contact: robtag@unisa.it Supplementary information: Supplementary data are available at Bioinformatics online.

A multi-step approach to time series analysis and gene expression clustering

RAICONI, Giancarlo;TAGLIAFERRI, Roberto
2006

Abstract

ABSTRACT Motivation: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. Results: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering genemicroarray data. Themethod,which provides auser-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. Availability: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. Contact: robtag@unisa.it Supplementary information: Supplementary data are available at Bioinformatics online.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/1520370
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