A highly efficient approach to protein interactome mapping based on collaborative filtering framework.
A highly efficient approach to protein interactome mapping based on collaborative filtering framework.
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基于协同过滤框架的高效蛋白质相互作用组图谱方法
DOI：
10.1038/srep07702
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发表时间：
2015-01-09
影响因子：
4.6
通讯作者：
Zhu Q
中科院分区：
文献类型：
--
作者：
Luo X;You Z;Zhou M;Li S;Leung H;Xia Y;Zhu Q
人们非常需要蛋白质-蛋白质相互作用（PPI）的全面图谱，以深入了解基本的细胞生物学过程和疾病的病理学。精细设置的小规模实验不仅非常昂贵，而且尽管精度很高，但识别大量相互作用组的效率却很低。高通量筛选技术可实现质子泵抑制剂的高效鉴定；然而，从这些数据中进一步提取有用知识的愿望导致了二进制相互作用组映射的问题。事实证明，基于网络拓扑的方法可以非常有效地解决这个问题；然而，它们的性能在稀疏的假定 PPI 网络上显着恶化。受基于协同过滤 (CF) 的方法成功解决大型稀疏评分矩阵个性化推荐问题的推动，这项工作旨在实现一种高效的基于 CF 的二元交互组映射方法。为了实现这一点，我们首先为此提出了一个CF框架。在此框架下，我们将给定的数据建模为相互作用组权重矩阵，其中提取相关蛋白质的特征向量。利用它们，我们设计了重新调整的余弦系数来模拟所涉及蛋白质之间的邻域间相似性，以进行映射过程。三个大型稀疏数据集的实验结果表明，所提出的方法显着优于几种复杂的基于拓扑的方法。
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.
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影响因子：
4.6
作者：
Pitre, Sylvain;Hooshyar, Mohsen;Schoenrock, Andrew;Samanfar, Bahram;Jessulat, Matthew;Green, James R.;Dehne, Frank;Golshani, Ashkan
通讯作者：
Golshani, Ashkan
影响因子：
14.9
作者：
Chatr-Aryamontri A;Breitkreutz BJ;Heinicke S;Boucher L;Winter A;Stark C;Nixon J;Ramage L;Kolas N;O'Donnell L;Reguly T;Breitkreutz A;Sellam A;Chen D;Chang C;Rust J;Livstone M;Oughtred R;Dolinski K;Tyers M
通讯作者：
Tyers M
影响因子：
14.9
作者：
Keshava Prasad TS;Goel R;Kandasamy K;Keerthikumar S;Kumar S;Mathivanan S;Telikicherla D;Raju R;Shafreen B;Venugopal A;Balakrishnan L;Marimuthu A;Banerjee S;Somanathan DS;Sebastian A;Rani S;Ray S;Harrys Kishore CJ;Kanth S;Ahmed M;Kashyap MK;Mohmood R;Ramachandra YL;Krishna V;Rahiman BA;Mohan S;Ranganathan P;Ramabadran S;Chaerkady R;Pandey A
通讯作者：
Pandey A
影响因子：
12.3
作者：
Hart GT;Ramani AK;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
12.3
作者：
Martin D;Brun C;Remy E;Mouren P;Thieffry D;Jacq B
通讯作者：
Jacq B