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
Ensemble Based Data Fusion for Gene Function Prediction
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Molecular Function Prediction Using Neighborhood Features
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
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein function prediction by integrating multiple kernels
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Advances in biotechnology have made available multitudes of heterogeneous proteomic and genomic data. Integrating these heterogeneous data sources, to automatically infer the function of proteins, is a fundamental challenge in computational biology. Several approaches represent each data source with a kernel (similarity) function. The resulting kernels are then integrated to determine a composite kernel, which is used for developing a function prediction model. Proteins are also found to have multiple roles and functions. As such, several approaches cast the protein function prediction problem within a multi-label learning framework. In our work we develop an approach that takes advantage of several unlabeled proteins, along with multiple data sources and multiple functions of proteins. We develop a graph-based transductive multi-label classifier (TMC) that is evaluated on a composite kernel, and also propose a method for data integration using the ensemble framework, called transductive multi-label ensemble classifier (TMEC). The TMEC approach trains a graph-based multi-label classifier for each individual kernel, and then combines the predictions of the individual models. Our contribution is the use of a bi-relational directed graph that captures relationships between pairs of proteins, between pairs of functions, and between proteins and functions. We evaluate the ability of TMC and TMEC to predict the functions of proteins by using two yeast datasets. We show that our approach performs better than recently proposed protein function prediction methods on composite and multiple kernels.