The University of Dublin | Trinity College -- Ollscoil Átha Cliath | Coláiste na Tríonóide
Trinity's Access to Research Archive
Home :: Log In :: Submit :: Alerts ::

School of Computer Science and Statistics >
Computer Science >
Computer Science Technical Reports >

Please use this identifier to cite or link to this item:

Title: Discovering genome expression patterns with self-organizing neural networks
Author: Azuaje, Francisco
Keywords: Computer Science
Issue Date: 2002
Publisher: Trinity College Dublin, Department of Computer Science
Citation: Azuaje, Francisco, 'Discovering genome expression patterns with self-organizing neural networks'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2002-30, 2002, pp1-14
Series/Report no.: Computer Science Technical Report
Abstract: Self-organizing neural networks represent a family of useful clusteringbased classification methods in several application domains. One such technique is the Kohonen Self-Organizing Feature Map (SOM) (Kohonen, 2001), which has become one of the most successful approaches to analysing genomic expression data. This model is relatively easy to implement and evaluate, computationally inexpensive and scalable. In addition, it exhibits significant advantages in comparison to other options. For instance, unlike hierarchical clustering it facilitates an automatic detection and inspection of clusters. Unlike Bayesian-based clustering it does not require prior hypotheses or knowledge about the data under consideration. Compared to the k-means clustering algorithm, the SOM exemplifies a robust and structured classification process. - [Introduction]
Description: Originally published: Azuaje F. “Discovering Genome Expression Patterns With Self-Organizing Neural Networks”, in Understanding and Using Microarray Analysis Techniques: A Practical Guide, Berrar D, Dubitzky W and Granzow M, editors, London: Springer Verlag, 2002 [Chapter 15]
Appears in Collections:Computer Science Technical Reports

Files in This Item:

File Description SizeFormat
TCD-CS-2002-30.pdf151.79 kBAdobe PDFView/Open

This item is protected by original copyright

Please note: There is a known bug in some browsers that causes an error when a user tries to view large pdf file within the browser window. If you receive the message "The file is damaged and could not be repaired", please try one of the solutions linked below based on the browser you are using.

Items in TARA are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback