Computational Intelligence Approaches for Pattern Discovery in Biological Systems

Brief Bioinform. 2008 Jul;9(4):307-16. doi: 10.1093/bib/bbn021. Epub 2008 May 5.


Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Computational Biology / methods*
  • Computational Biology / trends*
  • Computer Simulation
  • Models, Biological*
  • Pattern Recognition, Automated / methods*
  • Pattern Recognition, Automated / trends*
  • Systems Biology / trends*