ArticleInternational journal of molecular sciences2019
iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 82 citations in OpenAlex.
- Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.ACS omega · 2025Article
- Virulence arresting drug discovery by strategies targeting bacterial virulence: Mainly focusing on quorum-sensing interference and biofilm inhibition.Journal of pharmaceutical analysis · 2025Review
- Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.Molecular diversity · 2025Review
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
- Prediction and validation of nanowire proteins inComputational and structural biotechnology journal · 2025Article
- MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information.Scientific reports · 2024Article
- Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.Briefings in bioinformatics · 2024Article
- CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.Briefings in bioinformatics · 2024Article
- TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides.BMC bioinformatics · 2023Article
- StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens.BMC bioinformatics · 2023Article
- Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.Computational and structural biotechnology journal · 2023Review
- Empirical comparison and analysis of machine learning-based approaches for druggable protein identification.EXCLI journal · 2023Review
- TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus.PloS one · 2023Article
- StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy.Scientific reports · 2022Article
- Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.iScience · 2022Article
- SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.Pharmaceutics · 2022Article
- A survey on computational taste predictors.European food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A · 2022Review
- UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.International journal of molecular sciences · 2021Article
- iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.International journal of molecular sciences · 2021Article
- iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.
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What OpenQuestion holds
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.