Iterative Cluster Analysis of Protein Interaction Data
Bioinformatics
A tutorial on spectral clustering
Statistics and Computing
Active learning for microarray data
International Journal of Approximate Reasoning
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Optimistic active learning using mutual information
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incorporating diversity and density in active learning for relevance feedback
ECIR'07 Proceedings of the 29th European conference on IR research
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-class ensemble-based active learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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The high-throughput technologies have led to vast amounts of protein-protein interaction (PPI) data, and a number of approaches based on PPI networks have been proposed for protein function prediction. However, these approaches do not work well if annotated proteins are scarce in the networks. To address this issue, we propose an active learning based approach that uses graph-based centrality metrics to select proper candidates for labeling. We first cluster a PPI network by using the spectral clustering algorithm and select some proper candidates for labeling within each cluster, and then apply a collective classification algorithm to predict protein function based on these annotated proteins. Experiments over two real datasets demonstrate that the active learning based approach achieves better prediction performance by choosing more informative proteins for labeling. Experimental results also validate that betweenness centrality is more effective than degree centrality and closeness centrality in most cases.