Clustering binary codes to express the biochemical properties of amino acids

Huaiguo Fu, Engelbert Mephu Nguifo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We study four kinds of binary codes of amino acids (AA). Two codes of them are based respectively on biochemical properties, and the two others are generated with artificial intelligence (AI) methods, and are based on protein structures and alignment, and on Dayhoff matrix. In order to give a global significance of each binary code, we use a hierarchical clustering method to generate different clusters of each binary codes of amino acids. Each cluster is examined with biochemical properties to give an explanation on the similarity between amino acids that it contains. To validate our examination, a decision tree based machine learning system is used to characterize the AA clusters obtained with each binary codes. From this experimentation, it comes out that one of the AI based codes allows to obtain clusters that have significant biochemical properties. As a consequence, it appears that even if attributes of binary codes generated with AI methods, do not separately correspond to a biochemical property, they can be significant in the whole. Conversely binary codes based on biochemical properties can be insignificant when forming a whole.

Original languageEnglish
Title of host publicationIntelligent Information Processing II - IFIP TC12/WG12.3 International Conference on Intelligent Information Processing, IIP 2004
PublisherSpringer
Pages279-282
Number of pages4
ISBN (Print)038723151X, 9780387231518
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventIFIP TC12/WG12.3 International Conference on Intelligent Information Processing, IIP 2004 - Beijing, China
Duration: 21 Oct 200423 Oct 2004

Publication series

NameIFIP Advances in Information and Communication Technology
Volume163
ISSN (Print)1868-4238

Conference

ConferenceIFIP TC12/WG12.3 International Conference on Intelligent Information Processing, IIP 2004
Country/TerritoryChina
CityBeijing
Period21/10/200423/10/2004

Keywords

  • Amino acids
  • Bioinformatics and AI
  • Classification
  • Clustering

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