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Title: Improving network topology-based protein interactome mapping via collaborative filtering
Authors: Luo, X 
Ming, Z
You, Z
Li, S 
Xia, Y
Leung, H 
Keywords: Assessment
Collaborative filtering
Functional similarity weight
Inter-neighborhood similarity
Network topology
Protein interactome
Protein-protein interaction
Issue Date: 2015
Publisher: Elsevier
Source: Knowledge-based systems, 2015, v. 90, p. 23-32 How to cite?
Journal: Knowledge-based systems 
Abstract: 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.
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2015.10.003
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