Challenges for Intelligent Systems in Biology

Authors:
Russ B. Altman;Alfonso Valencia;Satoru Miyano;Shoba Ranganathan
Affiliations:
-;-;-;-
Venue:
IEEE Intelligent Systems
Year:
2001

Citing 2
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References

Intelligent data analysis
Combining Semantic Web technologies with Multi-Agent Systems for integrated access to biological resources

Journal of Biomedical Informatics
Applications of artificial intelligence in bioinformatics: A review

Expert Systems with Applications: An International Journal
On the implementation of a biologizing intelligent system

CIS'04 Proceedings of the First international conference on Computational and Information Science
Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection

Artificial Intelligence in Medicine
Review article: Computational intelligence techniques in bioinformatics

Computational Biology and Chemistry

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Abstract

Biological processes have produced the ultimate intelligent system (us), and now we are trying to understand biology (and ourselves) by building intelligent systems. Intelligent systems research in biology strives to understand how living systems perform difficult tasks routinely (ranging from molecular phenomena such as protein-folding to organism-level phenomena such as cognition). The definition of intelligent systems in biology can lead to hours of debate. Some--the lumpers--say that all high-performance systems that do something difficult with (orto) biological data should be considered intelligent systems. Others--the splitters--insist that the term "intelligent system" should be reserved for systems using the methods typically associated with modern AI. For this article, I will be a lumper. However, some systems are clearly more intelligent than others.