| Autor: |
Hüseyin Anil Gündüz, Martin Binder, Xiao-Yin To, René Mreches, Bernd Bischl, Alice C. McHardy, Philipp C. Münch, Mina Rezaei |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Communications Biology, Vol 6, Iss 1, Pp 1-12 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2399-3642 |
| DOI: |
10.1038/s42003-023-05310-2 |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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