Protein–Protein Interaction Prediction Based on Spectral Radius and General Regression Neural Network

2021 ◽  
Vol 20 (3) ◽  
pp. 1657-1665
Author(s):  
Hanxiao Xu ◽  
Da Xu ◽  
Naiqian Zhang ◽  
Yusen Zhang ◽  
Rui Gao
Keyword(s):  
Neural Network ◽  
Spectral Radius ◽  
Protein Interaction ◽  
General Regression Neural Network ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
General Regression

scholarly journals Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

2021 ◽  
Author(s):  
Guofeng Lv ◽  
Zhiqiang Hu ◽  
Yanguang Bi ◽  
Shaoting Zhang
Keyword(s):  
Neural Network ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Poor Performance ◽  
Evaluation Framework ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Real World Datasets ◽  
Novel Protein

The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms. Existing methods suffer from significant performance degradation when tested in unseen dataset. In this paper, we investigate the problem and find that it is mainly attributed to the poor performance for inter-novel-protein interaction prediction. However, current evaluations overlook the inter-novel-protein interactions, and thus fail to give an instructive assessment. As a result, we propose to address the problem from both the evaluation and the methodology. Firstly, we design a new evaluation framework that fully respects the inter-novel-protein interactions and gives consistent assessment across datasets. Secondly, we argue that correlations between proteins must provide useful information for analysis of novel proteins, and based on this, we propose a graph neural network based method (GNN-PPI) for better inter-novel-protein interaction prediction. Experimental results on real-world datasets of different scales demonstrate that GNN-PPI significantly outperforms state-of-the-art PPI prediction methods, especially for the inter-novel-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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