Clustering, assessment and validation: an application to gene expression data

A. Ciaramella; S. Cocozza; E. Lorio; G. Miele; F. Napolitano; M. Pinelli; G. Raiconi; R. Tagliaferri; IEEE

doi:10.1109/IJCNN.2007.4371199

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Clustering, assessment and validation: an application to gene expression data

A. Ciaramella, S. Cocozza, E. Lorio, G. Miele, F. Napolitano, M. Pinelli, G. Raiconi, R. Tagliaferri and IEEE

2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, pp.1613-1618

IEEE International Joint Conference on Neural Networks (IJCNN)

01/01/2007

DOI: https://doi.org/10.1109/IJCNN.2007.4371199

Abstract

Computer Science

Computer Science, Artificial Intelligence

Computer Science, Software Engineering

Science & Technology

Technology

In this work a multi-step approach for clustering assessment, visualization and data validation is introduced. Three main approaches for data clustering are used and compared: K-means, Self Organizing Maps and Probabilistic Principal Surfaces. A model explorer approach with different similarity measures is used to obtain the best parameters of the methods. The approach is used to identify genes periodically expressed in tumors related to the human cell cycle. Finally, clusters are validated by using GO Term information.

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Title: Clustering, assessment and validation: an application to gene expression data
Creators - without role: A. Ciaramella - University of Salerno
S. Cocozza
E. Lorio
G. Miele - University of Naples Federico II
F. Napolitano - University of Salerno
M. Pinelli
G. Raiconi - University of Salerno
R. Tagliaferri - University of Salerno
IEEE
Publication Details: 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, pp.1613-1618
Series: IEEE International Joint Conference on Neural Networks (IJCNN)
Publisher: IEEE
Number of pages: 3
Identifiers: 9941331708331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Conference proceeding