A Finite State Automata Based Technique for Protein Classification Rules Induction

  • Psomopoulos F
  • Diplaris S
  • Mitkas P
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Abstract

See, stats, and : https : / / www . researchgate . net / publication / 228692951 A protein Article CITATIONS 9 READS 27 3 , including : Fotis . Psomopoulos Aristotle 48 SEE Pericles . Mitkas Aristotle 272 , 635 SEE All . The . All - text and , letting . ABSTRACT An important challenge in modern functional proteomics is the prediction of the functional behavior of proteins . Motifs in protein chains can make such a prediction possible . The correlation between protein properties and their motifs is not always obvious , since more than one motifs can exist within a protein chain . Thus , the behavior of a protein is a function of many motifs , where some overpower others . In this paper a data - mining approach for motif - based classification of proteins is presented . A new classification rules inducing algorithm that exploits finite state automata is introduced . First , data are modeled by terms of prefix tree acceptors , which are later merged into finite state automata . Finally , we propose a new algorithm for the induction of protein classification rules from finite state automata . The data - mining model is trained and tested using various protein and protein class subsets , as well as the whole dataset of known proteins and protein classes . Results indicate the efficiency of our technique compared to other known data - mining algorithms .

Author-supplied keywords

  • Performance Keywords finite state automata
  • bioinformatics
  • data mining
  • motifs
  • protein classification
  • proteomics

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Authors

  • F Psomopoulos

  • S Diplaris

  • P A Mitkas

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