Predicting individualized tissue gene expression profiles with multi-task learning

He, G.; Chen, M.; Bian, Y.; Yang, E.

doi:10.1101/2022.10.19.512838

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bioRxiv · 10.1101/2022.10.19.512838

Predicting individualized tissue gene expression profiles with multi-task learning

He, G.·Chen, M.·Bian, Y.·Yang, E.

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Abstract

Predicting tissue expression profiles from peripheral surrogate samples, especially blood transcriptome, has become an effective alternative when invasive procedures are not ideal. However, existing approaches ignore tissue-shared intrinsic relevance, inevitably limiting predictive performance. Here, we propose a unified deep learning-based multi-task learning framework, Multi-tissue Transcriptome Mapping (MTM), enabling the prediction of individualized expression profiles from any available tissue of an individual. By jointly leveraging individualized cross-tissue information through multi-task learning, MTM achieves superior sample-level and gene-level performance. With the high prediction accuracy and the ability to preserve individualized biological variations, MTM could facilitate both fundamental and clinical biomedical research.

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He, G., Chen, M., Bian, Y., Yang, E.. 2022-10-21. Predicting individualized tissue gene expression profiles with multi-task learning. https://doi.org/10.1101/2022.10.19.512838

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