A New Method for Protein Complex Detection Based on Multi-Source Information

Biophysics ◽  
2018 ◽  
Vol 06 (04) ◽  
pp. 67-75
Author(s):  
杨 于
Keyword(s):  
Protein Complex ◽  
New Method ◽  
Source Information ◽  
Protein Complex Detection ◽  
Complex Detection

A New Method for Protein Complex Detection Based on Multi-Source Information

Biophysics ◽  
2018 ◽  
Vol 06 (04) ◽  
pp. 67-75
Author(s):  
杨 于
Keyword(s):  
Protein Complex ◽  
New Method ◽  
Source Information ◽  
Protein Complex Detection ◽  
Complex Detection

scholarly journals Protein complex detection with semi-supervised learning in protein interaction networks

2011 ◽  
Vol 9 (Suppl 1) ◽  
pp. S5 ◽  
Author(s):  
Lei Shi ◽  
Xiujuan Lei ◽  
Aidong Zhang
Keyword(s):  
Supervised Learning ◽  
Protein Interaction ◽  
Protein Complex ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Complex Detection ◽  
Complex Detection

PC-SENE: A node embedding based method for protein complex detection

2018 ◽  
Author(s):  
Xiaoxia Liu ◽  
Zhihao Yang ◽  
Shengtian Sang ◽  
Lei Wang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Protein Complex ◽  
Protein Complex Detection ◽  
Complex Detection

Protein Complex Detection Based on Integrated Strategy

2015 ◽  
Vol 12 (11) ◽  
pp. 4822-4827 ◽  
Author(s):  
Yang Yu ◽  
Jie Liu ◽  
Lili Shan ◽  
Hang Li ◽  
Bo Song
Keyword(s):  
Protein Complex ◽  
Protein Complex Detection ◽  
Integrated Strategy ◽  
Complex Detection

Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2040010 ◽  
Author(s):  
Heng Yao ◽  
Jihong Guan ◽  
Tianying Liu
Keyword(s):  
Protein Interaction ◽  
Protein Complex ◽  
Protein Complexes ◽  
Empirical Evaluation ◽  
High Rate ◽  
Detection Methods ◽  
Convolutional Network ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Complex Detection

Identifying protein complexes is an important issue in computational biology, as it benefits the understanding of cellular functions and the design of drugs. In the past decades, many computational methods have been proposed by mining dense subgraphs in Protein–Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents accurately detecting complexes directly from the raw PINs. In this paper, we propose a denoising approach for protein complex detection by using variational graph auto-encoder. First, we embed a PIN to vector space by a stacked graph convolutional network (GCN), then decide which interactions in the PIN are credible. If the probability of an interaction being credible is less than a threshold, we delete the interaction. In such a way, we reconstruct a reliable PIN. Following that, we detect protein complexes in the reconstructed PIN by using several typical detection methods, including CPM, Coach, DPClus, GraphEntropy, IPCA and MCODE, and compare the results with those obtained directly from the original PIN. We conduct the empirical evaluation on four yeast PPI datasets (Gavin, Krogan, DIP and Wiphi) and two human PPI datasets (Reactome and Reactomekb), against two yeast complex benchmarks (CYC2008 and MIPS) and three human complex benchmarks (REACT, REACT_uniprotkb and CORE_COMPLEX_human), respectively. Experimental results show that with the reconstructed PINs obtained by our denoising approach, complex detection performance can get obviously boosted, in most cases by over 5%, sometimes even by 200%. Furthermore, we compare our approach with two existing denoising methods (RWS and RedNemo) while varying different matching rates on separate complex distributions. Our results show that in most cases (over 2/3), the proposed approach outperforms the existing methods.

scholarly journals Improved method for protein complex detection using bottleneck proteins

2013 ◽  
Vol 13 (S1) ◽  
Author(s):  
Jaegyoon Ahn ◽  
Dae Hyun Lee ◽  
Youngmi Yoon ◽  
Yunku Yeu ◽  
Sanghyun Park
Keyword(s):  
Protein Complex ◽  
Improved Method ◽  
Protein Complex Detection ◽  
Complex Detection
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