Integrating supervised and unsupervised learning in self organizing maps for gene expression data analysis

Seferina Mavroudi, Andrei Dragomir, Stergios Papadimitriou, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Recently, Self Organizing Maps have been a popular approach to analyze gene expression data. Our paper presents an improved SOM-based algorithm called Supervised Network Self Organizing Map (sNet-SOM), which overcomes the main drawbacks of existing techniques by adaptively determining the number of clusters with a dynamic extension process and integrating unsupcrvised and supervised learning in an effort to make use of prior knowledge on data. The process is driven by an inhomogeneous measure that balances unsupervised/supervised learning and model complexity criteria. Multiple models are dynamically constructed by the algorithm, each corresponding to an unsupervised/supervised balance, model selection criteria being used to select the optimum one. The design allows us to effectively utilize multiple functional class labeling.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsOkyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu
PublisherSpringer Verlag
Pages262-270
Number of pages9
ISBN (Print)3540404082, 9783540404088
DOIs
StatePublished - Jan 1 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2714
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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