Multi-Class Protein Fold Classification Using a New Ensemble Machine Learning Approach

Aik Choon Tan[1] (
David Gilbert[1] (
Yves Deville[2] (

[1]Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, Glasgow, G12 8QQ, Scotland, United Kingdom
[2]Department of Computing Science and Engineering, Universit$\acute{\rm e}$ catholique de Louvain Place Sainte Barbe 2, B-1348 Louvain-la-Neuve, Belgium


Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources.

[ Full-text PDF | Table of Contents ]

Japanese Society for Bioinformatics