Active learning for human protein-protein interaction prediction

Background: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. Results: Random forest (RF) has previously been shown to be effective for predicting proteinprotein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions i...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Thahir P. Mohamed, Jaime G. Carbonell, Madhavi Ganapathiraju
• Keywords: Active Learning, BMCBI 2010, Protein-protein Interactions, Proteins