ArticleBriefings in bioinformatics2024
MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction.Frontiers in artificial intelligence · 2026Article
- Cat-PIPpred: Pro-Inflammatory Peptide Predictor Integrating CatBoost and Cross-Modal Feature Fusion.International journal of molecular sciences · 2025Article
- Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction.BMC biology · 2025Article
- MFP-MFL: Leveraging Graph Attention and Multi-Feature Integration for Superior Multifunctional Bioactive Peptide Prediction.International journal of molecular sciences · 2025Article
- Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Inflammatory responses may lead to tissue or organ damage, and proinflammatory peptides (PIPs) are signaling peptides that can induce such responses. Many diseases have been redefined as inflammatory diseases. To identify PIPs more efficiently, we expanded the dataset and designed an ensemble learning model with manually encoded features. Specifically, we adopted a more comprehensive feature encoding method and considered the actual impact of certain features to filter them. Identification and prediction of PIPs were performed using an ensemble learning model based on five different classifiers. The results show that the model's sensitivity, specificity, accuracy, and Matthews correlation coefficient are all higher than those of the state-of-the-art models. We named this model MultiFeatVotPIP, and both the model and the data can be accessed publicly at https://github.com/ChaoruiYan019/MultiFeatVotPIP. Additionally, we have developed a user-friendly web interface for users, which can be accessed at http://www.bioai-lab.com/MultiFeatVotPIP.
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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.