ArticleInternational journal of molecular sciences2020
Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
29 citing papers in PubMed, 59 citations in OpenAlex.
- Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein-Protein Interaction Detection.International journal of molecular sciences · 2026Review
- MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- ASCE-PPIS: a protein-protein interaction sites predictor based on equivariant graph neural network with fusion of structure-aware pooling and graph collapse.Bioinformatics (Oxford, England) · 2025Article
- Article
- Genome-wide analysis of the BoBZR1 family genes and transcriptome analysis in Brassica oleracea.Scientific reports · 2025Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Genome-wide identification and in-silico expression analysis of CCO gene family in sunflower (Helianthus annnus) against abiotic stress.Plant molecular biology · 2024Article
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.BMC bioinformatics · 2023Article
- Evaluation of GPT and BERT-based models on identifying proteinprotein interactions in biomedical text.ArXiv · 2023Article
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- Article
- DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network.The protein journal · 2023Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning.Communications biology · 2023Article
- Quantitative approaches for decoding the specificity of the human T cell repertoire.Frontiers in immunology · 2023Review
- PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.