ArticleInterdisciplinary sciences, computational life sciences2022
PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches.
Article in Interdisciplinary sciences, computational life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 33 citations in OpenAlex.
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- Multimodal molecular features enable interpretable umami peptide screening and structure-activity analysis.Current research in food science · 2026Article
- TPpred-CMvL: prediction of multi-functional therapeutic peptide using contrast multi-view learning.BMC biology · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- misORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.Interdisciplinary sciences, computational life sciences · 2025Article
- iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning.Genomics, proteomics & bioinformatics · 2025Article
- Protein language model-based prediction for plant miRNA encoded peptides.PeerJ. Computer science · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.BMC bioinformatics · 2024Article
- AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors.Bioinformatics (Oxford, England) · 2024Article
- CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.Briefings in bioinformatics · 2024Article
- Characterisation of a novel crustin isoform from mud crab,In silico pharmacology · 2023Article
- MFPPDB: a comprehensive multi-functional plant peptide database.Frontiers in plant science · 2023Article
- PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.PLoS computational biology · 2022Article
- The dynamic landscape of peptide activity prediction.Computational and structural biotechnology journal · 2022Review
- i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Anti-parasitic peptides (APPs) have been regarded as promising therapeutic candidate drugs against parasitic diseases. Due to the fact that the experimental techniques for identifying APPs are expensive and time-consuming, there is an urgent need to develop a computational approach to predict APPs on a large scale. In this study, we provided a computational method, termed PredAPP (Prediction of Anti-Parasitic Peptides) that could effectively identify APPs using an ensemble of well-performed machine learning (ML) classifiers. Firstly, to solve the class imbalance problem, a balanced training dataset was generated by the undersampling method. We found that the balanced dataset based on cluster centroid achieved the best performance. Then, nine groups of features and six ML algorithms were combined to generate 54 classifiers and the output of these classifiers formed 54 feature representations, and in each feature group, we selected the feature representation with best performance for classification. Finally, the selected feature representations were integrated using logistic regression algorithm to construct the prediction model PredAPP. On the independent dataset, PredAPP achieved accuracy and AUC of 0.880 and 0.922, respectively, compared to 0.739 and 0.873 of AMPfun, a state-of-the-art method to predict APPs. The web server of PredAPP is freely accessible at http://predapp.xialab.info and https://github.com/xialab-ahu/PredAPP .
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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.