Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
An integrated probabilistic model for functional prediction of proteins
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
A statistical framework for genomic data fusion
Bioinformatics
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Expert Systems with Applications: An International Journal
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
Prediction of movement direction in crude oil prices based on semi-supervised learning
Decision Support Systems
Random spanning trees and the prediction ofweighted graphs
The Journal of Machine Learning Research
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 12.07 |
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as protein-protein interactions, genetic interactions, or co-participation in a protein complex, etc. Relying on similarities between nodes, each graph can be used independently for prediction of protein function. However, since different graphs contain partly independent and partly complementary information about the problem at hand, one can enhance the total information extracted by combining all graphs. In this paper, we propose a method for integrating multiple graphs within a framework of semi-supervised learning. The method alternates between minimizing the objective function with respect to network output and with respect to combining weights. We apply the method to the task of protein functional class prediction in yeast. The proposed method performs significantly better than the same algorithm trained on any single graph.