An artificial immune system for evolving amino acid clusters tailored to protein function prediction

A. Secker*, A. A. Freitas, J. Timmis, E. Clark, D. R. Flower

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem, where the protein functions to be predicted correspond to classes that are arranged in a hierarchical structure, in the form of a class tree. The main contribution of this paper is to propose a new Artificial Immune System that creates a new representation for proteins, in order to maximize the predictive accuracy of a hierarchical classification algorithm applied to the corresponding protein function prediction problem.

Original languageEnglish
Title of host publicationArtificial Immune Systems - 7th International Conference, ICARIS 2008, Proceedings
PublisherSpringer Nature
Number of pages12
ISBN (Print)3540850716, 9783540850717
Publication statusPublished - 2008
Event7th International Conference on Artificial Immune Systems, ICARIS 2008 - Phuket, Thailand
Duration: 10 Aug 200813 Aug 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5132 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Artificial Immune Systems, ICARIS 2008
Period10 Aug 200813 Aug 2008


  • Artificial immune systems
  • Bioinformatics
  • Classification
  • Clustering
  • Data mining

Cite this