Modeling disease progression via fused sparse group lasso

Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

90 Scopus citations

Abstract

Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in Alzheimer's disease research. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. We show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex programming technique to solve the non-convex formulations. Our extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression.

Original languageEnglish (US)
Title of host publicationKDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages1095-1103
Number of pages9
DOIs
StatePublished - Sep 14 2012
Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 16 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
CountryChina
CityBeijing
Period8/12/128/16/12

Keywords

  • alzheimer's disease
  • cognitive score
  • fused lasso
  • multi-task learning
  • regression
  • sparse group lasso

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'Modeling disease progression via fused sparse group lasso'. Together they form a unique fingerprint.

  • Cite this

    Zhou, J., Liu, J., Narayan, V. A., & Ye, J. (2012). Modeling disease progression via fused sparse group lasso. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1095-1103). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2339530.2339702