title: A highly efficient approach to protein interactome mapping based on collaborative filtering framework.
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.
authors.affiliation: 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.
authors.forename: Xin
authors.initials: X
authors.lastname: Luo
authors.affiliation: X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China.
authors.forename: Zhuhong
authors.initials: Z
authors.lastname: You
authors.affiliation: M. Zhou is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Mengchu
authors.initials: M
authors.lastname: Zhou
authors.affiliation: X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China.
authors.forename: Shuai
authors.initials: S
authors.lastname: Li
authors.affiliation: X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China.
authors.forename: Hareton
authors.initials: H
authors.lastname: Leung
authors.affiliation: X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China.
authors.forename: Yunni
authors.initials: Y
authors.lastname: Xia
authors.affiliation: X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China.
authors.forename: Qingsheng
authors.initials: Q
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins