NEC for gene expression analysis

  • Authors:

  • R. Amato;A. Ciaramella;N. Deniskina;C. Del Mondo;D. di Bernardo;C. Donalek;G. Longo;G. Mangano;G. Miele;G. Raiconi;A. Staiano;R. Tagliaferri

  • Affiliations:

  • Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Matematica e Informatica, University of Salerno, Fisciano, Salerno, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Telethon Institute for Genetics and Medicine, Naples, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Matematica e Informatica, University of Salerno, Fisciano, Salerno, Italy;Dipartimento di Scienze Fisiche, University of Naples “Federico II”, Naples, Italy;Dipartimento di Matematica e Informatica, University of Salerno, Fisciano, Salerno, Italy

  • Venue:
  • WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
  • Year:
  • 2005

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Abstract

Aim of this work is to apply a novel comprehensive data mining machine learning tool to preprocess and to interpret gene expression data. Furthermore, some visualization facilities are provided. The data mining framework consists of two main parts: preprocessing and clustering-agglomerating phases. To the first phase belong a noise filtering procedure and a non-linear PCA Neural Network for feature extraction. The second phase is used to accomplish an unsupervised clustering based on a hierarchy of two approaches: a Probabilistic Principal Surfaces to obtain the rough regions of interesting points and a Fisher-Negentropy information based approach to agglomerate the regions previously found in order to discover substructures present in the data. Experiments on gene microarray data are made. Several experiments are shown varying the threshold, needed by the agglomerative clustering, to understand the structure of the analyzed data set.