Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36267
Title: A highly efficient approach to protein interactome mapping based on collaborative filtering framework
Authors: Luo, X
You, ZH
Zhou, MC
Li, S 
Leung, H 
Xia, YN
Zhu, QS
Issue Date: 2015
Source: Scientific reports, 9 2015, v. 5, no. , p. 1-10
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.
Publisher: Nature Publishing Group
Journal: Scientific reports 
EISSN: 2045-2322
DOI: 10.1038/srep07702
Rights: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
The following publication Luo, X., You, Z., Zhou, M. et al. A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework. Sci Rep 5, 7702 (2015) is available at https://dx.doi.org/10.1038/srep07702
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