ArticleScientific reports2022
SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- StackPVP: a stacked ensemble classification framework for predicting phage virion proteins using integrated evolutionary features.Frontiers in microbiology · 2026Article
- Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.ACS omega · 2025Article
- Computational function prediction of bacteria and phage proteins.Microbiology and molecular biology reviews : MMBR · 2025Review
- M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.BMC bioinformatics · 2025Article
- The gut virome and human health: From diversity to personalized medicine.Engineering microbiology · 2025Review
- Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy.Scientific reports · 2025Article
- Optimizing phage therapy with artificial intelligence: a perspective.Frontiers in cellular and infection microbiology · 2025Review
- TCellPredX: A Novel Approach for Accurate Prediction of Hepatitis C Virus Linear T Cell Epitopes.ACS omega · 2024Article
- StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.BMC bioinformatics · 2024Article
- A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.Interdisciplinary sciences, computational life sciences · 2024Article
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.BMC bioinformatics · 2024Article
- Leveraging a meta-learning approach to advance the accuracy of NaScientific reports · 2024Article
- PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation.Frontiers in microbiology · 2024Article
- StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists.Scientific reports · 2023Article
- StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens.BMC bioinformatics · 2023Article
- Article
- Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset.Scientific reports · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository ( https://github.com/saeed344/SCORPION ).
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