ArticleJournal of healthcare engineering2018
Predicting Interactions between Virus and Host Proteins Using Repeat Patterns and Composition of Amino Acids.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 44 citations in OpenAlex.
- 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 robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information.Computational and structural biotechnology journal · 2023Article
- Predicting Protein-Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence.Biology · 2022Article
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 2022Article
- RoFDT: Identification of Drug-Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest.Biology · 2022Article
- Article
- LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor.PloS one · 2022Article
- Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.Computational and structural biotechnology journal · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.Medical & biological engineering & computing · 2021Article
- A scheme for inferring viral-host associations based on codon usage patterns identifies the most affected signaling pathways during COVID-19.Journal of biomedical informatics · 2021Article
- Epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa.medRxiv : the preprint server for health sciences · 2021Article
- ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor.Frontiers in molecular biosciences · 2021Article
- SARS-CoV-2-human protein-protein interaction network.Informatics in medicine unlocked · 2020Article
- Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.Computational and structural biotechnology journal · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous methods for predicting protein-protein interactions (PPIs) were mainly focused on PPIs within a single species, but PPIs across different species have recently emerged as an important issue in some areas such as viral infection. The primary focus of this study is to predict PPIs between virus and its targeted host, which are involved in viral infection. We developed a general method that predicts interactions between virus and host proteins using the repeat patterns and composition of amino acids. In independent testing of the method with PPIs of new viruses and hosts, it showed a high performance comparable to the best performance of other methods for single virus-host PPIs. In comparison of our method with others using same datasets, our method outperformed the others. The repeat patterns and composition of amino acids are simple, yet powerful features for predicting virus-host PPIs. The method developed in this study will help in finding new virus-host PPIs for which little information is available.
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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 OpenQuestion graph.