A self-supervised deep learning method for data-efficient training in genomics
© 2023. Springer Nature Limited..
Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
Communications biology - 6(2023), 1 vom: 11. Sept., Seite 928 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gündüz, Hüseyin Anil [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 13.09.2023 Date Revised 19.11.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s42003-023-05310-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Binder, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a To, Xiao-Yin |e verfasserin |4 aut | |
| 700 | 1 | |a Mreches, René |e verfasserin |4 aut | |
| 700 | 1 | |a Bischl, Bernd |e verfasserin |4 aut | |
| 700 | 1 | |a McHardy, Alice C |e verfasserin |4 aut | |
| 700 | 1 | |a Münch, Philipp C |e verfasserin |4 aut | |
| 700 | 1 | |a Rezaei, Mina |e verfasserin |4 aut | |
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