An artificial immune system for clustering amino acids in the context of protein function classification

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

Research output: Contribution to journalArticlepeer-review

8 Citations (SciVal)

Abstract

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. The protein functions to be predicted correspond to classes that are arranged in a hierarchical structure (this takes 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
Pages (from-to)103-123
Number of pages21
JournalJournal of Mathematical Modelling and Algorithms
Volume8
Issue number2
DOIs
Publication statusPublished - Jun 2009

Keywords

  • Artificial immune systems
  • Bioinformatics
  • Classification
  • Clustering
  • Optimisation

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