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

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 personal

SUBMITTER: Liu T 

PROVIDER: S-EPMC12559380 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature