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Knowledge discovery with artificial immune systems for hierarchical multi-label classification of protein functions

Alves, Roberto T. and Delgado, Myriam and Freitas, Alex A. (2010) Knowledge discovery with artificial immune systems for hierarchical multi-label classification of protein functions. In: Sobrevilla, P. and Aranda, J. and Xambo, S., eds. International Conference on Fuzzy Systems. IEEE, pp. 182-196. ISBN 978-1-4244-6919-2. E-ISBN 978-1-4244-6921-5. (doi:10.1109/FUZZY.2010.5584298) (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:30655)

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/10.1109/FUZZY.2010.5584298

Abstract

This work presents a system for knowledge discovery from protein databases, based on an Artificial Immune System. The discovered rules have the advantage of representing comprehensible knowledge to biologist users. This task leads to a very challenging problem since a protein can be assigned multiple classes (functions or Gene Ontology (GO) terms) across several levels of the GO's term hierarchy. To solve this problem we present two versions of an algorithm called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), which is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification. The first version of MHC-AIS builds a global classifier to predict all classes in the dataset, whilst the second version builds a local classifier to predict each class. The proposed versions and an algorithm chosen for comparison are evaluated on a protein dataset, and the results show that MHC-AIS outperformed the compared algorithm in general.

Item Type: Book section
DOI/Identification number: 10.1109/FUZZY.2010.5584298
Uncontrolled keywords: determinacy analysis, Craig interpolants
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 21 Sep 2012 09:49 UTC
Last Modified: 04 Feb 2020 04:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/30655 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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