An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A statistical framework for genomic data fusion
Bioinformatics
Semi-supervised protein classification using cluster kernels
Bioinformatics
Fast protein classification with multiple networks
Bioinformatics
Hierarchical multi-label prediction of gene function
Bioinformatics
Combining kernels for classification
Combining kernels for classification
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Protein functional class prediction with a combined graph
Expert Systems with Applications: An International Journal
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Ensemble Based Data Fusion for Gene Function Prediction
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Learning protein functions from bi-relational graph of proteins and function annotations
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Image annotation using bi-relational graph of images and semantic labels
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multi-label learning with incomplete class assignments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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High-throughput experimental techniques produce several heterogeneous proteomic and genomic datasets. To computationally annotate proteins, it is necessary and promising to integrate these heterogeneous data sources. Some methods transform these data sources into different kernels or feature representations. Next, these kernels are linearly (or non-linearly) combined into a composite kernel. The composite kernel is utilized to develop a predictive model to infer the function of proteins. A protein can have multiple roles and functions (or labels). Therefore, multi-label learning methods are also adapted for protein function prediction. We develop a transductive multi-label classifier (TMC) to predict multiple functions of proteins using several unlabeled proteins. We also propose a method called transductive multi-label ensemble classifier (TMEC) for integrating the different data sources using an ensemble approach. TMEC trains a graph-based multi-label classifier on each single data source and then combines the predictions of the individual classifiers. We use a directed bi-relational graph to captures three types of relationships between pairs of proteins, between pairs of functions, and between proteins and functions. We evaluate the effectiveness of TMC and TMEC to predict the functions of proteins on three benchmarks. We show that our approaches perform better than recently proposed protein function prediction methods on composite and multiple kernels.