ArticleBioinformatics (Oxford, England)2018
Prediction of human-Bacillus anthracis protein-protein interactions using multi-layer neural network.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- HVIface: sequence-based deep learning for decoding human-virus protein-protein interfaces.Frontiers in bioinformatics · 2026Article
- Multiple Strategies Confirm the Anti Hepatocellular Carcinoma Effect of Cinnamic Acid Based on the PI3k-AKT Pathway.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions.Computational and structural biotechnology journal · 2025Article
- A predictive approach for host-pathogen interactions using deep learning and protein sequences.Virusdisease · 2024Article
- GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.BMC genomics · 2024Article
- Computational Network Inference for Bacterial Interactomics.mSystems · 2022Review
- Predicting protein-protein interactions between banana and Fusarium oxysporum f. sp. cubense race 4 integrating sequence and domain homologous alignment and neural network verification.Proteome science · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Article
- Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.Computational and structural biotechnology journal · 2020Article
- A Bipartite Network Module-Based Project to Predict Pathogen-Host Association.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Triplet amino acids have successfully been included in feature selection to predict human-HPV protein-protein interactions (PPI). The utility of supervised learning methods is curtailed due to experimental data not being available in sufficient quantities. Improvements in machine learning techniques and features selection will enhance the study of PPI between host and pathogen. Results: We present a comparison of a neural network model versus SVM for prediction of host-pathogen PPI based on a combination of features including: amino acid quadruplets, pairwise sequence similarity, and human interactome properties. The neural network and SVM were implemented using Python Sklearn library. The neural network model using quadruplet features and other network features outperformance the SVM model. The models are tested against published predictors and then applied to the human-B.anthracis case. Gene ontology term enrichment analysis identifies immunology response and regulation as functions of interacting proteins. For prediction of Human-viral PPI, our model (neural network) is a significant improvement in overall performance compared to a predictor using the triplets feature and achieves a good accuracy in predicting human-B.anthracis PPI. Availability and implementation: All code can be downloaded from ftp://ftp.sanbi.ac.za/machine_learning/. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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