Title: Inference of domain-domain interactions by matrix factorisation and domain-level features

Authors: Tu Kien T. Le; Osamu Hirose; Lan Anh T. Nguyen; Thammakorn Saethang; Vu Anh Tran; Xuan Tho Dang; Duc Luu Ngo; Mamoru Kubo; Yoichi Yamada; Kenji Satou

Addresses: Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan ' Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan

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.

Keywords: 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.

DOI: 10.1504/IJFIPM.2014.068174

International Journal of Functional Informatics and Personalised Medicine, 2014 Vol.4 No.3/4, pp.259 - 273

Received: 07 May 2013
Accepted: 16 Feb 2014
Published online: 22 Mar 2015 *

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