A novel algorithm for classifying protein structure familiar by using the graph mining approach

  • Sun Yuan Hsieh
  • , Chia Wei Lee
  • , Zong Ying Yang
  • , Heng Wei Wang
  • , Jun Han Yu

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

1 Citation (Scopus)

Abstract

Protein structural classification is critical in bioinformatics. In this study, a simple and connected graph was used to represent a 3D protein structure in which each node represented an amino acid and each edge represented a contact distance between two amino acids. The B-factor (atomic displacement parameters) was then used to substantially reduce the number of nodes and edges in each graph representation. A graph mining approach was applied to determine the critical subgraphs among these graphs, which can be applied to classify protein structural families. An experimental study was conducted in which characteristic substructural patterns were identified in several protein families in the SCOP database.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Methodologies - 11th International Conference, ICIC 2015, Proceedings
EditorsVitoantonio Bevilacqua, De-Shuang Huang, Prashan Premaratne
PublisherSpringer Verlag
Pages723-729
Number of pages7
ISBN (Print)9783319221793
DOIs
Publication statusPublished - 2015
Event11th International Conference on Intelligent Computing, ICIC 2015 - Fuzhou, China
Duration: 2015 Aug 202015 Aug 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9225
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Intelligent Computing, ICIC 2015
Country/TerritoryChina
CityFuzhou
Period15-08-2015-08-23

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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