ArticleJournal of computer-aided molecular design2020
ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations.
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 23 papers.
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Who cites it
23 citing papers in PubMed.
- METFAN: Multisource Enhanced Therapeutic Peptide Function Prediction via Adapter Network.ACS omega · 2025Article
- Cat-PIPpred: Pro-Inflammatory Peptide Predictor Integrating CatBoost and Cross-Modal Feature Fusion.International journal of molecular sciences · 2025Article
- Novel antimicrobial peptide HFIAP-1 mutant as a β-lactamase inhibitor against extended-spectrum β-lactamases of Escherichia coli: a comprehensive in-silico approach.Archives of microbiology · 2025Article
- Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.Bioinformatics (Oxford, England) · 2025Article
- 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
- Unsupervised encoding selection through ensemble pruning for biomedical classification.BioData mining · 2023Article
- MFPPDB: a comprehensive multi-functional plant peptide database.Frontiers in plant science · 2023Article
- NeuroCNN_GNB: an ensemble model to predict neuropeptides based on a convolution neural network and Gaussian naive Bayes.Frontiers in genetics · 2023Article
- PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.PLoS computational biology · 2022Article
- BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.Briefings in bioinformatics · 2022Article
- MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Hierarchical representation for PPI sites prediction.BMC bioinformatics · 2022Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.Briefings in bioinformatics · 2022Article
- TUPDB: Target-Unrelated Peptide Data Bank.Interdisciplinary sciences, computational life sciences · 2021Article
- Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction.BMC bioinformatics · 2021Article
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
- PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.International journal of molecular sciences · 2021Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate the probabilistic scores by using the random forest models employing eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by linearly combining the resultant eight probabilistic scores. Evaluated through independent test, the ProIn-Fuse yielded an accuracy of 0.746, which was 10% higher than those obtained by the state-of-the-art PIP predictors. The proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse .
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