Background: With the rapid accumulation of proteomic and genomic datasets in terms of genome-scale features and interaction networks through high-throughput experimental techniques, the process of manual predicting functional properties of the proteins has become increasingly cumbersome, and computational methods to automate this annotation task are urgently needed. Most of the approaches in predicting functional properties of proteins require to either identify a reliable set of labeled proteins with similar attribute features to unannotated proteins, or to learn from a full-labeled protein interaction network with a large amount of labeled nodes. However, acquiring such labels can be very difficult in practice, especially for multi-label protein function prediction problems. Learning with only a few labeled data can lead to poor performance as limited supervision knowledge can be obtained from the similar proteins or from the connections between them. To effectively annotate proteins even in the paucity of labeled data, it is of interest to take advantage of all data sources that are available in this problem setting, including interaction networks, attribute feature information, correlations of functional labels, and unlabeled data.

Results: In this paper, we show that the underlying nature of predicting functional properties of proteins using various data sources of relational data is a typical Collective Classification (CC) problem in machine learning. The protein functional prediction task with limited annotation is then cast into a Semi-supervised Multi-label Collective Classification (SMCC) framework. As such, we propose a novel Generative Model based SMCC algorithm, called GM-SMCC, to effectively compute the label probability distributions and predict functional properties to unannotated proteins. To further boost the predicting performance, we extend the method in an ensemble manner, called EGM-SMCC , by utilizing multiple heterogeneous networks with various latent linkages constructed to explicitly model the relationships among the nodes for effectively propagating the supervision knowledge from labeled to unlabeled nodes.

Conclusion: Experimental results on KDD Cup tasks of 2001 predicting the functions and localization of a protein to a given yeast gene demonstrate the effectiveness of the proposed method. In the comparison, we find that the performances of the proposed algorithms are better than the other compared algorithms.