Modeling multi-stage disease progression and identifying genetic risk factors via a novel collaborative learning method.

ABSTRACT:

Motivation

Alzheimer's disease (AD) typically progresses gradually for ages rather than suddenly. Thus, staging AD progression in different phases could aid in accurate diagnosis and treatment. In addition, identifying genetic variations that influence AD is critical to understanding the pathogenesis. However, staging the disease progression and identifying genetic variations is usually handled separately.

Results

To address this limitation, we propose a novel sparse multi-stage multi-task mixed-effects collaborative longitudinal regression method (MSColoR). Our method jointly models long disease progression as a multi-stage procedure and identifies genetic risk factors underpinning this complex trajectory. Specifically, MSColoR models multi-stage disease progression using l

SUBMITTER: Xi D 

PROVIDER: S-EPMC11784593 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature