scholarly journals Conserved network motifs allow protein-protein interaction prediction

2004 ◽  
Vol 20 (18) ◽  
pp. 3346-3352 ◽  
Author(s):  
I. Albert ◽  
R. Albert
Keyword(s):  
Protein Interaction ◽  
Network Motifs ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction

scholarly journals Protein-Protein Interaction: Prediction, Design, and Modulation

2012 ◽  
Vol 28 (10) ◽  
pp. 2363-2380 ◽  
Author(s):  
ZHANG Chang-Sheng ◽  
◽  
LAI Lu-Hua ◽  
◽  
Keyword(s):  
Protein Interaction ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction

Improving Protein-protein Interaction Prediction by Incorporating 3D Genome Information

2021 ◽  
pp. 511-520
Author(s):  
Zehua Guo ◽  
Kai Su ◽  
Liangjie Liu ◽  
Xianbin Su ◽  
Mofan Feng ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
3D Genome ◽  
Protein Interaction Prediction ◽  
Genome Information

scholarly journals Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

2019 ◽  
Vol 35 (14) ◽  
pp. i305-i314 ◽  
Author(s):  
Muhao Chen ◽  
Chelsea J -T Ju ◽  
Guangyu Zhou ◽  
Xuelu Chen ◽  
Tianran Zhang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Mutual Influence ◽  
State Of The Art ◽  
Supplementary Information ◽  
Interaction Type ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Ppi Prediction ◽  
Limited Coverage

AbstractMotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Ortholog-based protein-protein interaction prediction and its application to inter-species interactions

2008 ◽  
Vol 9 (Suppl 12) ◽  
pp. S11 ◽  
Author(s):  
Sheng-An Lee ◽  
Cheng-hsiung Chan ◽  
Chi-Hung Tsai ◽  
Jin-Mei Lai ◽  
Feng-Sheng Wang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Species Interactions ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction
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