AUTHOR=Liang Jiaqi , Xue Zhao , Zhou Wenchao , Guo Xiangjie , Wen Yalu 

TITLE=Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer’s disease

JOURNAL=Frontiers in Genetics

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1538544

DOI=10.3389/fgene.2025.1538544

ISSN=1664-8021

ABSTRACT=IntroductionCorrelated phenotypes may have both shared and unique causal factors, and jointly modeling these phenotypes can enhance prediction performance by enabling efficient information transfer.MethodsWe propose an auto-branch multi-task learning model within a deep learning framework for the simultaneous prediction of multiple correlated phenotypes. This model dynamically branches from a hard parameter sharing structure to prevent negative information transfer, ensuring that parameter sharing among phenotypes is beneficial.ResultsThrough simulation studies and analysis of seven Alzheimer's disease-related phenotypes, our method consistently outperformed Multi-Lasso model, single-task learning approaches, and commonly used hard parameter sharing models with predefine shared layers. These analyses also reveal that while genetic contributions across phenotypes are similar, the relative influence of each genetic factor varies substantially among phenotypes.