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

This study comprehensively evaluates the performance of five 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...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2016-11, Vol.13 (6), p.1045-1058
Main Authors: Fabris, Fabio, Freitas, Alex A., Tullet, Jennifer M. A.
Format: Article
Language:English
Subjects:
Ageing
Aging
Aging - genetics
Algorithms
Bayesian analysis
Biological models (mathematics)
Biological system modeling
Classification
Classification algorithms
Clustering algorithms
Computer Simulation
Data Mining - methods
Databases, Genetic
Datasets
dependence networks
Gene Expression Profiling - methods
hierarchical classification
Humans
Longevity
Machine Learning
Models, Genetic
Models, Statistical
Pattern Recognition, Automated - methods
Prediction algorithms
Probabilistic logic
Probabilistic methods
protein classification
Proteins
Proteome - genetics
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Summary:This study comprehensively evaluates the performance of five 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 longevity-related genes. We conclude that the LHC-PCT algorithm ranks better across several tests (seven 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.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2015.2505288