An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes

Fabris, Fabio and Freitas, Alex A. and Tullet, Jennifer M A (2015) An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics, . p. 1. ISSN 1545-5963. (In press) (doi:https://doi.org/10.1109/TCBB.2015.2505288) (Full text available)

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Abstract

This study comprehensively evaluates the performance of 5 types of probabilistic hierarchical classification methods used for predicting Gene Ontology (GO) terms related to ageing. Of those tested, a new hybrid of a Local Hierarchical Classifier (LHC) and the Predictive Clustering Tree algorithm (LHC-PCT) had the best predictive accuracy results. We also tested the impact of two types of variations in most hierarchical classification algorithms, namely: (a) changing the base algorithm (we tested Naive Bayes and Support Vector Machines), and the impact of (b) using or not the Correlation based Feature Selection (CFS) algorithm in a pre-processing step. In total, we evaluated the predictive performance of 17 variations of hierarchical classifiers across 15 datasets of ageing and longevityrelated genes. We conclude that the LHC-PCT algorithm ranks better across several tests (7 out of 12). In addition, we interpreted the models generated by the PCT algorithm to show how hierarchical classification algorithms can be used to extract biological insights out of the ageing-related datasets that we compiled.

Item Type: Article
Subjects: Q Science > Q Science (General)
Divisions: Faculties > Sciences > School of Biosciences
Faculties > Sciences > School of Biosciences > Cell & Developmental Biology Group
Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Faculties > Sciences > School of Computing > Systems Architecture Group
Depositing User: Jennifer Tullet
Date Deposited: 11 Nov 2016 16:52 UTC
Last Modified: 14 Nov 2016 11:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58594 (The current URI for this page, for reference purposes)
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