Journal
KNOWLEDGE-BASED SYSTEMS
Volume 90, Issue -, Pages 23-32Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2015.10.003
Keywords
Protein-protein interaction; Protein interactome; Assessment; Prediction; Network topology; Inter-neighborhood similarity; Functional similarity weight; Collaborative filtering
Categories
Funding
- Young Scientist Foundation of Chongqing [cstc2014kjrc-qnrc40005]
- National Natural Science Foundation of China [61202347, 61472051, 61272194, 61373086, 61401385]
- Postdoctoral Science Funded Project of Chongqing [Xm2014043]
- Fundamental Research Funds for the Central Universities [106112015CDJXY180005, CD-JZR12180012]
- China Postdoctoral Science Foundation [2014M562284]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120191120030]
- Natural Science Foundation Project of CQ CSTC [cstc2012jjA40002, cstc2013jcyjA40046]
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High-throughput screening (HTS) techniques enable massive identification of protein-protein interactions (PPIs). Nonetheless, it is still intractable to observe the full mapping of PPIs. With acquired PPI data, scalable and inexpensive computation-based approaches to protein interactome mapping (PIM), which aims at increasing the data confidence and predicting new PPIs, are desired in such context. Network topology-based approaches prove to be highly efficient in addressing this issue; yet their performance deteriorates significantly on sparse HTS-PPI networks. This work aims at implementing a highly efficient network topology-based approach to PIM via collaborative filtering (CF), which is a successful approach to addressing sparse matrices for personalized-recommendation. The motivation is that the problems of PIM and personalized-recommendation have similar solution spaces, where the key is to model the relationship among involved entities based on incomplete information. Therefore, it is expected to improve the performance of a topology-based approach on sparse HTS-PPI networks via integrating the idea of CF into it. We firstly model the HTS-PPI data into an incomplete matrix, where each entry describes the interactome weight between corresponding protein pair. Based on it, we transform the functional similarity weight in topology-based approaches into the inter-neighborhood similarity (I-Sim) to model the protein-protein relationship. Finally, we apply saturation-based strategies to the I-Sim model to achieve the CF-enhanced topology-based (CFT) approach to PIM. (C) 2015 Elsevier B.V. All rights reserved.
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