Trinity College Dublin, Department of Computer Science
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
Computer Science Technical Report TCD-CS-2002-30
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]
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]
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