"Frustratingly easy" domain adaptation for cross-species transcription factor binding prediction.

ABSTRACT:

Motivation

Understanding how DNA sequence encodes gene regulation remains a central challenge in genomics. While deep learning models can predict regulatory activity from sequence with high accuracy, their generalizability across species-and thus their ability to capture fundamental biological principles-remains limited. Cross-species prediction provides a powerful test of model robustness and offers a window into conserved regulatory logic, but effectively bridging species-specific genomic differences remains a major barrier.

Results

We present MORALE, a novel and scalable domain adaptation framework that significantly advances cross-species prediction of transcription factor (TF) binding. By aligning statistical moments of sequence embeddings across species, MORALE enables

SUBMITTER: Ebeid MM 

PROVIDER: S-EPMC12154900 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature