ArticleMolecules (Basel, Switzerland)2018
Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- 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
- 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
- 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
- Large-scale comparative review and assessment of computational methods for phage virion proteins identification.EXCLI journal · 2022Review
- CAF-Associated Paracrine Signaling Worsens Outcome and Potentially Contributes to Chemoresistance in Epithelial Ovarian Cancer.Frontiers in oncology · 2022Article
- Article
- Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.Frontiers in public health · 2022Article
- Phages and Enzybiotics in Food Biopreservation.Molecules (Basel, Switzerland) · 2021Review
- Application of machine learning in bacteriophage research.BMC microbiology · 2021Review
- Identification of Novel COVID-19 Biomarkers by Multiple Feature Selection Strategies.Computational and mathematical methods in medicine · 2021Article
- VirionFinder: Identification of Complete and Partial Prokaryote Virus Virion Protein From Virome Data Using the Sequence and Biochemical Properties of Amino Acids.Frontiers in microbiology · 2021Article
- Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.Journal of computer-aided molecular design · 2020Article
- PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.Cells · 2020Article
- Predicting Cell Wall Lytic Enzymes Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
- SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.Molecular therapy. Nucleic acids · 2019Article
- mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins.
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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.