ArticleJournal of computer-aided molecular design2020
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.
Article in Journal of computer-aided molecular design, 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
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Phage-derived proteins: Advancing food safety through biocontrol and detection of foodborne pathogens.Comprehensive reviews in food science and food safety · 2025Review
- Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.Scientific reports · 2024Article
- StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.Bioinformatics (Oxford, England) · 2024Article
- PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.Bioinformatics (Oxford, England) · 2023Article
- PhaGAA: an integrated web server platform for phage genome annotation and analysis.Bioinformatics (Oxford, England) · 2023Article
- Prediction of Phage Virion Proteins Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2023Article
- Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.iScience · 2022Article
- DeePVP: Identification and classification of phage virion proteins using deep learning.GigaScience · 2022Article
- AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.Scientific reports · 2022Article
- SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.Scientific reports · 2022Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- Resolving the structure of phage-bacteria interactions in the context of natural diversity.Nature communications · 2022Article
- MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.Briefings in bioinformatics · 2022Article
- STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.Briefings in bioinformatics · 2022Article
- Recent development of machine learning-based methods for the prediction of defensin family and subfamily.EXCLI journal · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phage virion protein (PVP) perforate the host cell membrane and eventually culminates in cell rupture thereby releasing replicated phages. The accurate identification of PVP is thus a crucial step towards improving our understanding of the biological function and mechanisms of PVPs. Therefore, it is desirable to develop a computational method that is capable of fast and accurate identification of PVPs. To address this, we propose a novel sequence-based meta-predictor employing probabilistic information (referred herein as the Meta-iPVP) for the accurate identification of PVPs. Particularly, efficient feature representation approach was used to generate discriminative probabilistic features from four machine learning (ML) algorithms making use of seven feature encodings. To the best of our knowledge, the Meta-iPVP is the first meta-based approach that has been developed for PVP prediction. Independent test results indicated that the Meta-iPVP could discern important characteristics between PVPs and non-PVPs as well as achieving the best accuracy and MCC of 0.817 and 0.642, respectively, which corresponds to 6-10% and 14-21% improvements over existing PVP predictors. As such, this demonstrates that the proposed Meta-iPVP is a more efficient, robust and promising for the identification of PVPs. The predictive model is deployed as a publicly accessible Meta-iPVP webserver freely available online at http://camt.pythonanywhere.com/Meta-iPVP .
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