Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease.

INTRODUCTION

Correlated phenotypes may have both shared and unique causal factors, and jointly modeling these phenotypes can enhance prediction performance by enabling efficient information transfer.

METHODS

We 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.

RESULTS

Through 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.