A complex bio-networks of the function profile of genes

Charles C H Liu, I-Jen Chiang, Jau Min Wong, Ginni Hsiang Chun Tsai, Tsau Young Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel model of concept representation using a multilevel geometric structure, which is called Latent Semantic Networks. Given a set of documents, the associations among frequently co-occurring terms in any of the documents define naturally a geometric complex, which can then be decomposed into connected components at various levels. This hierarchical model of knowledge representation was validated in the functional profiling of genes. Our approach excelled the traditional approach of vector-based document clustering by the geometrical forms of frequent itemsets generated by the association rules. The biological profiling of genes were a complex of concepts, which could be decomposed into primitive concepts, based on which the relevant literature could be clustered in adequate "resolution" of contexts. The hierarchical representation could be validated with tree-based biomedical ontological frameworks, which had been applied for years, and been recently enriched by the online availability of Unified Medical Language System (UMLS) and Gene Ontology (GO). Demonstration of the model and the clustering would be performed on the relevant GeneRIF (References into Function) document set of NOD2 gene. Our geometrical model is suitable for representation of bio-logical information, where hierarchical concepts in different complexity could be explored interactively according to the context of application and the various needs of the researchers. An online clustering search engine for use on general purpose and for biomedical use, managing the search results from Google or from PubMed, are constructed based on the methodology (http://ginni.bme.ntu.edu.tw). The hierarchical presentation of clustering results and the interactive graphical display of the contents of each cluster shows the merits of our approach.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages9-24
Number of pages16
Volume4070 LNBI
Publication statusPublished - 2006
Event2005 IEEE International Conference on Granular Computing - Beijing, China
Duration: Jul 25 2005Jul 27 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4070 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2005 IEEE International Conference on Granular Computing
CountryChina
CityBeijing
Period7/25/057/27/05

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

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  • Cite this

    Liu, C. C. H., Chiang, I-J., Wong, J. M., Tsai, G. H. C., & Lin, T. Y. (2006). A complex bio-networks of the function profile of genes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4070 LNBI, pp. 9-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4070 LNBI).