ArticleCells2020
PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.
Article in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions.BioData mining · 2026Article
- StackPVP: a stacked ensemble classification framework for predicting phage virion proteins using integrated evolutionary features.Frontiers in microbiology · 2026Article
- Empathi: embedding-based phage protein annotation tool by hierarchical assignment.Nature communications · 2025Article
- ProtPhage: a deep learning framework for phage viral protein identification and functional annotation.Briefings in bioinformatics · 2025Article
- VITALdb: to select the best viroinformatics tools for a desired virus or application.Briefings in bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
- PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.International journal of molecular sciences · 2024Article
- VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation.Briefings in bioinformatics · 2023Article
- Knowing and Naming: Phage Annotation and Nomenclature for Phage Therapy.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Review
- PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.Bioinformatics (Oxford, England) · 2023Article
- Prediction of Phage Virion Proteins Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2023Article
- Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.Scientific reports · 2022Article
- DeePVP: Identification and classification of phage virion proteins using deep learning.GigaScience · 2022Article
- SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.Scientific reports · 2022Article
- PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach.Viruses · 2022Article
- MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.Briefings in bioinformatics · 2022Article
- Recent development of machine learning-based methods for the prediction of defensin family and subfamily.EXCLI journal · 2022Review
- Large-scale comparative review and assessment of computational methods for phage virion proteins identification.EXCLI journal · 2022Review
- A survey on computational taste predictors.European food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.
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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.