Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Robust label propagation on multiple networks
IEEE Transactions on Neural Networks
Using the Gene Ontology hierarchy when predicting gene function
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Expert Systems with Applications: An International Journal
S3MKL: scalable semi-supervised multiple kernel learning for image data mining
Proceedings of the international conference on Multimedia
COSNet: a cost sensitive neural network for semi-supervised learning in graphs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Classification and annotation in social corpora using multiple relations
Proceedings of the 20th ACM international conference on Information and knowledge management
A bootstrapping method for learning from heterogeneous data
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
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
Protein function prediction using weak-label learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Synergistic effect of different levels of genomic data for cancer clinical outcome prediction
Journal of Biomedical Informatics
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)
Predicting Protein Functions from Protein Interaction Networks
International Journal of Knowledge Discovery in Bioinformatics
Sharpened graph ensemble for semi-supervised learning
Intelligent Data Analysis
Hi-index | 3.84 |
Motivation: Support vector machines (SVMs) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced. In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n3), and produces a dense matrix of size n × n. Results: We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similar to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method. Availability: Software and data will be available on request. Contact: shin@tuebingen.mpg.de