Inference of domain–domain interactions by matrix factorisation and domain–level features. (1st January 2014)

• Record Type: Journal Article
• Authors: Le, Tu Kien T.; Hirose, Osamu; Nguyen, Lan Anh T.; Saethang, Thammakorn; Tran, Vu Anh; Dang, Xuan Tho; Ngo, Duc Luu; Kubo, Mamoru; Yamada, Yoichi; Satou, Kenji
• Is Part Of: International journal of functional informatics and personalised medicine. Volume 4:Number 3/4(2014)
• Page Start: 259
• Page End: 273
• Publication Date: 2014-01-01
• Subjects: PPIs -- protein–protein interactions -- DDIs -- domain–domain interactions -- DDI inference -- latent learning model -- matrix factorisation -- link prediction -- low rank matrix -- domain–level features -- conditional sampling -- non–DDIs -- imbalanced training dataset -- biological features -- topological features -- domain networks; Biomedical engineering -- Periodicals; Bioinformatics -- Periodicals; 610.28
• DOI: 10.1504/IJFIPM.2014.068174
• Languages: English
• ISSNs: 1756-2104

Abstract

In the development of new drugs and improved treatment of diseases, it is essential to understand molecular networks in living organism. Especially, it is important to identify interacting domains among proteins to elucidate hidden functions for protein–protein interactions (PPIs). To date, a number of computational methods have been developed for predicting domain–domain interactions (DDIs) from known PPIs. However, they often contain a large number of false positives while the number of known structures of protein complexes is limited. In this study, we aim to develop a new method of predicting DDIs by a link prediction approach. By using a learning model including low rank matrices as latent features in combination with biological features and topological features of the domain network, the experimental results showed that our method achieved a good performance and the predicted DDIs have high fraction sharing rate with the ones known as true in gold–standard databases.