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Sci Rep. 2015 Jan 9;5:7702. doi: 10.1038/srep07702.

A highly efficient approach to protein interactome mapping based on collaborative filtering framework.

Author information

1
1] X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China [2] X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China.
2
X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China.
3
M. Zhou is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
4
X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China.

Abstract

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

PMID:
25572661
PMCID:
PMC4287731
DOI:
10.1038/srep07702
[Indexed for MEDLINE]
Free PMC Article

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