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Multi-label hierarchical classification of protein functions with artificial immune systems

Alves, Roberto T. and Delgado, Myriam and Freitas, Alex A. (2008) Multi-label hierarchical classification of protein functions with artificial immune systems. In: Bazzan, Ana L.C. and Craven, Mark and Martins, Natalia F., eds. Advances in Bioinformatics and Computational Biology Third Brazilian Symposium on Bioinformatics. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 1-12. ISBN 978-3-540-85556-9. E-ISBN 978-3-540-85557-6. (doi:10.1007/978-3-540-85557-6_1) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:24053)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/978-3-540-85557-6_1

Abstract

This work proposes two versions of an Artificial Immune System (AIS) - a relatively recent computational intelligence paradigm - for predicting protein functions described in the Gene Ontology (GO). The GO has functional classes (GO terms) specified in the form of a directed acyclic graph, which leads to a very challenging multi-label hierarchical classification problem where a protein can be assigned multiple classes (functions, GO terms) across several levels of the GO's term hierarchy. Hence, the proposed approach, called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification. The first version of the MHC-AIS builds a global classifier to predict all classes in the application domain, whilst the second version builds a local classifier to predict each class. In both versions of the MHC-AIS the classifier is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users. The two MHC-AIS versions are evaluated on a dataset of DNA-binding and ATPase proteins.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-540-85557-6_1
Uncontrolled keywords: classification, data mining, artificial immune systems
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:12 UTC
Last Modified: 16 Feb 2021 12:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/24053 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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