The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Support Vector Data Description
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Kernel methods for predicting protein--protein interactions
Bioinformatics
The SSEA server for protein secondary structure alignment
Bioinformatics
An analysis of diversity measures
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Interaction-site prediction for protein complexes
Bioinformatics
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This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse biological data. Gold Standard data sets frequently employed for this task contain a high proportion of instances related to ribosomal proteins. We demonstrate that this situation biases the classification results and additionally that the prediction of non-ribosomal based PPI is a much more difficult task. In order to improve the performance of this subtask we have integrated more biological data into the classification process, including data from mRNA expression experiments and protein secondary structure information. Furthermore we have investigated several strategies for combining diverse one-class classification (OCC) models generated from different subsets of biological data. The weighted average combination approach exhibits the best results, significantly improving the performance attained by any single classification model evaluated.