ArticleNAR genomics and bioinformatics2021
A large-scale comparative study on peptide encodings for biomedical classification.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Molecular fingerprints are strong models for peptide function prediction.Bioinformatics (Oxford, England) · 2026Article
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Genomic data representations for horizontal gene transfer detection.NAR genomics and bioinformatics · 2025Article
- Classification of bioactive peptides: A systematic benchmark of models and encodings.Computational and structural biotechnology journal · 2024Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Superior protein thermophilicity prediction with protein language model embeddings.NAR genomics and bioinformatics · 2023Article
- Unsupervised encoding selection through ensemble pruning for biomedical classification.BioData mining · 2023Article
- A parametric approach for molecular encodings using multilevel atomic neighborhoods applied to peptide classification.NAR genomics and bioinformatics · 2023Article
- Eleven quick tips for data cleaning and feature engineering.PLoS computational biology · 2022Article
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- Multivalent binding kinetics resolved by fluorescence proximity sensing.Communications biology · 2022Article
- Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research.Reproductive sciences (Thousand Oaks, Calif.) · 2022Review
- Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.BMC bioinformatics · 2021Article
- Chaos game representation and its applications in bioinformatics.Computational and structural biotechnology journal · 2021Review
- MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities.Biology methods & protocols · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Owing to the great variety of distinct peptide encodings, working on a biomedical classification task at hand is challenging. Researchers have to determine encodings capable to represent underlying patterns as numerical input for the subsequent machine learning. A general guideline is lacking in the literature, thus, we present here the first large-scale comprehensive study to investigate the performance of a wide range of encodings on multiple datasets from different biomedical domains. For the sake of completeness, we added additional sequence- and structure-based encodings. In particular, we collected 50 biomedical datasets and defined a fixed parameter space for 48 encoding groups, leading to a total of 397 700 encoded datasets. Our results demonstrate that none of the encodings are superior for all biomedical domains. Nevertheless, some encodings often outperform others, thus reducing the initial encoding selection substantially. Our work offers researchers to objectively compare novel encodings to the state of the art. Our findings pave the way for a more sophisticated encoding optimization, for example, as part of automated machine learning pipelines. The work presented here is implemented as a large-scale, end-to-end workflow designed for easy reproducibility and extensibility. All standardized datasets and results are available for download to comply with FAIR standards.
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Registered trials
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