Unsupervised encoding selection through ensemble pruning for biomedical classification
Abstract Background Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BioData Mining |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13040-022-00317-7 |
| _version_ | 1797865382413860864 |
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| author | Sebastian Spänig Alexander Michel Dominik Heider |
| author_facet | Sebastian Spänig Alexander Michel Dominik Heider |
| author_sort | Sebastian Spänig |
| collection | DOAJ |
| description | Abstract Background Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers leverage artificial intelligence to circumvent the cumbersome, wet-lab-based identification and automate the detection of promising candidates. However, the prediction of a peptide’s function is not limited to antimicrobial efficiency. To date, multiple studies successfully classified additional properties, e.g., antiviral or cell-penetrating effects. In this light, ensemble classifiers are employed aiming to further improve the prediction. Although we recently presented a workflow to significantly diminish the initial encoding choice, an entire unsupervised encoding selection, considering various machine learning models, is still lacking. Results We developed a workflow, automatically selecting encodings and generating classifier ensembles by employing sophisticated pruning methods. We observed that the Pareto frontier pruning is a good method to create encoding ensembles for the datasets at hand. In addition, encodings combined with the Decision Tree classifier as the base model are often superior. However, our results also demonstrate that none of the ensemble building techniques is outstanding for all datasets. Conclusion The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models. Consequently, researchers can use the workflow for unsupervised encoding selection and ensemble creation. Ultimately, the extensible workflow can be used as a plugin for the PEPTIDE REACToR, further establishing it as a versatile tool in the domain. |
| first_indexed | 2024-04-09T23:07:06Z |
| format | Article |
| id | doaj.art-1d007c3a0bd4446dbd9986c86630713d |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-04-09T23:07:06Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-1d007c3a0bd4446dbd9986c86630713d2023-03-22T10:35:34ZengBMCBioData Mining1756-03812023-03-0116112010.1186/s13040-022-00317-7Unsupervised encoding selection through ensemble pruning for biomedical classificationSebastian Spänig0Alexander Michel1Dominik Heider2Data Science in Biomedicine, Department of Mathematics and Computer Science, University of MarburgData Science in Biomedicine, Department of Mathematics and Computer Science, University of MarburgData Science in Biomedicine, Department of Mathematics and Computer Science, University of MarburgAbstract Background Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers leverage artificial intelligence to circumvent the cumbersome, wet-lab-based identification and automate the detection of promising candidates. However, the prediction of a peptide’s function is not limited to antimicrobial efficiency. To date, multiple studies successfully classified additional properties, e.g., antiviral or cell-penetrating effects. In this light, ensemble classifiers are employed aiming to further improve the prediction. Although we recently presented a workflow to significantly diminish the initial encoding choice, an entire unsupervised encoding selection, considering various machine learning models, is still lacking. Results We developed a workflow, automatically selecting encodings and generating classifier ensembles by employing sophisticated pruning methods. We observed that the Pareto frontier pruning is a good method to create encoding ensembles for the datasets at hand. In addition, encodings combined with the Decision Tree classifier as the base model are often superior. However, our results also demonstrate that none of the ensemble building techniques is outstanding for all datasets. Conclusion The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models. Consequently, researchers can use the workflow for unsupervised encoding selection and ensemble creation. Ultimately, the extensible workflow can be used as a plugin for the PEPTIDE REACToR, further establishing it as a versatile tool in the domain.https://doi.org/10.1186/s13040-022-00317-7Biomedical classificationAntimicrobial peptidesEncodingsMachine learningEnsemble learning |
| spellingShingle | Sebastian Spänig Alexander Michel Dominik Heider Unsupervised encoding selection through ensemble pruning for biomedical classification BioData Mining Biomedical classification Antimicrobial peptides Encodings Machine learning Ensemble learning |
| title | Unsupervised encoding selection through ensemble pruning for biomedical classification |
| title_full | Unsupervised encoding selection through ensemble pruning for biomedical classification |
| title_fullStr | Unsupervised encoding selection through ensemble pruning for biomedical classification |
| title_full_unstemmed | Unsupervised encoding selection through ensemble pruning for biomedical classification |
| title_short | Unsupervised encoding selection through ensemble pruning for biomedical classification |
| title_sort | unsupervised encoding selection through ensemble pruning for biomedical classification |
| topic | Biomedical classification Antimicrobial peptides Encodings Machine learning Ensemble learning |
| url | https://doi.org/10.1186/s13040-022-00317-7 |
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