ArticleiScience2022
Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.BMC biology · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model.Communications biology · 2024Article
- A BERT-based approach for identifying anti-inflammatory peptides using sequence information.Heliyon · 2024Article
- Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry.Current research in food science · 2024Article
- Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity.Amino acids · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and efficient identification of anti-inflammatory peptides (AIPs) is crucial for the treatment of inflammation. Here, we proposed a two-layer stacking ensemble model, AIPStack, to effectively predict AIPs. At first, we constructed a new dataset for model building and validation. Then, peptide sequences were represented by hybrid features, which were fused by two amino acid composition descriptors. Next, the stacking ensemble model was constructed by random forest and extremely randomized tree as the base-classifiers and logistic regression as the meta-classifier to receive the outputs from the base-classifiers. AIPStack achieved an AUC of 0.819, accuracy of 0.755, and MCC of 0.510 on the independent set 3, which were higher than other AIP predictors. Furthermore, the essential sequence features were highlighted by the Shapley Additive exPlanation (SHAP) method. It is anticipated that AIPStack could be used for AIP prediction in a high-throughput manner and facilitate the hypothesis-driven experimental design.
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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.