ArticleFrontiers in immunology2018
PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.
Article in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 120 citations in OpenAlex.
- Targeting Amastigote and Trypomastigote Phases: Multi-Epitope Vaccine Strategy Against Trypanosoma cruzi.Biotechnology and applied biochemistry · 2026Article
- Cat-PIPpred: Pro-Inflammatory Peptide Predictor Integrating CatBoost and Cross-Modal Feature Fusion.International journal of molecular sciences · 2025Article
- Advancing therapeutic vaccines for chronic hepatitis B: Integrating reverse vaccinology and immunoinformatics.World journal of hepatology · 2025Review
- Computationally designed multi-epitope vaccine construct targeting the SARS-CoV-2 spike protein elicits robust immune responses in silico.Scientific reports · 2025Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.Briefings in bioinformatics · 2024Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.bioRxiv : the preprint server for biology · 2024Article
- Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.International journal of molecular sciences · 2023Review
- Unsupervised encoding selection through ensemble pruning for biomedical classification.BioData mining · 2023Article
- Article
- Anti-Cancer Peptides: Status and Future Prospects.Molecules (Basel, Switzerland) · 2023Review
- In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Characterisation of a novel crustin isoform from mud crab,In silico pharmacology · 2023Article
- Considering epitopes conservity in targeting SARS-CoV-2 mutations in variants: a novel immunoinformatics approach to vaccine design.Scientific reports · 2022Article
- MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
- Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.Briefings in bioinformatics · 2022Article
- SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.Computational and structural biotechnology journal · 2022Article
- Peptide-Based Vaccines for Tuberculosis.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.
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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.