Secker, A. and Davies, M.N. and Freitas, A.A. and Timmis, J. and Clark, E.B. and Flower, D.R. (2008) An artificial immune system for evolving amino acid clusters tailored to protein function prediction. In: Bentley, P.J. and Lee, D. and Jung, S., eds. Artificial Immune Systems. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Springer, pp. 242-253. ISBN 978-3-540-85071-7.
|The full text of this publication is not available from this repository. (Contact us about this Publication)|
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.
|Item Type:||Book section|
|Uncontrolled keywords:||artificial immune systems; data mining; bioinformatics; classification; clustering|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > Q Science (General) > Q335 Artificial intelligence
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing|
|Depositing User:||Maureen Cook|
|Date Deposited:||26 Mar 2009 09:43|
|Last Modified:||23 Feb 2010 18:11|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/15659 (The current URI for this page, for reference purposes)|
- Depositors only (login required):