A Self-Supervised Deep Learning Method for Data-Efficient Training in Genomics

MCML Authors

Hüseyin Anil Gündüz

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Martin Binder

→ Group Bernd Bischl
 Statistical Learning and Data Science

Xiao-Yin To

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Mina Rezaei

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

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.

article GBT+23

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Communications Biology

6.928. Sep. 2023.

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Authors

H. A. Gündüz • M. Binder • X.-Y. To • R. Mreches • B. Bischl • A. C. McHardy • P. C. Münch • M. Rezaei

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability