Multi-task Learning for Predicting Alzheimer’s Disease Progression

Award Number
Award Type
Status / Stage
7 February 2022 -
5 August 2025
Duration (calculated)
03 years 05 months
Funding Amount
Funder/Grant study page
Contracted Centre
University of Sheffield
Principal Investigator
Xulong Wang
WHO Catergories
Development of novel therapies
Models of Disease
Disease Type
Alzheimer's Disease (AD)

CPEC Review Info
Reference ID762
ResearcherReside Team


Award Number2784475
Status / StageActive
Start Date20220207
End Date20250805
Duration (calculated) 03 years 05 months
Funder/Grant study pageEPSRC
Contracted CentreUniversity of Sheffield
Funding Amount£0.00

Plain English Summary

Alzheimer’s disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying biomarkers that can track the progress of the disease has received much attentions in AD research. An accurate prediction of disease progression would facilitate optimal decision-making for clinicians and patients.

The aim of this project is to investigate multi-task learning approaches into predicting AD progression measured by the cognitive scores and selecting biomarkers predictive of the progression. This project will formulate the prediction problem as a multi-task regression problem by considering the approach of Temporal Group Lasso, which is a multi-task, regularised approach for the prediction of response variables that vary over time.


This objectives of this project include: 1) to investigate the concepts behind the Temporal Group LASSO and its related methods, as well as the type of potential applications in AD research. 2) to build up and implement a Temporal Group Lasso based AD progression model. 3) to evaluate the model using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). 4) to analysis and discuss its future potential.