In EDS ansehen

UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.

Gespeichert in:
Bibliographische Detailangaben
Titel: UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.
Autoren: Liu, Tianyu, Huang, Tinglin, Wang, Lijun, Lin, Yingxin, Ying, Rex, Zhao, Hongyu
Quelle: Nature Communications; 10/27/2025, Vol. 16 Issue 1, p1-14, 14p
Schlagwörter: GENE expression, MACHINE learning, STATISTICAL measurement, FORECASTING, NUCLEOTIDE sequence, PHENOTYPES
Abstract: Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a computational method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multi-omic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances. Here, authors present UNICORN, a multi-task learning framework that integrates foundation model embeddings and multi-omic data to accurately predict cell-type-specific gene expression and uncover biological insights. [ABSTRACT FROM AUTHOR]
Copyright of Nature Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a computational method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multi-omic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances. Here, authors present UNICORN, a multi-task learning framework that integrates foundation model embeddings and multi-omic data to accurately predict cell-type-specific gene expression and uncover biological insights. [ABSTRACT FROM AUTHOR]
ISSN:20411723
DOI:10.1038/s41467-025-64506-8