TY - JOUR
T1 - Modeling Alzheimer's disease progression with fused laplacian sparse group lasso
AU - Liu, Xiaoli
AU - Cao, Peng
AU - Gonçalves, André R.
AU - Zhao, Dazhe
AU - Banerjee, Arindam
N1 - Funding Information:
The research was supported by the National Natural Science Foundation of China (No.61502091), the Fundamental Research Funds for the Central Universities (Nos. N161604001 and N150408001). The research was also supported by NSF grants IIS-1563950, IIS-1447566, IIS-1447574, IIS-1422557, CCF-1451986, and CNS-1314560. Authors’ addresses: X. Liu, P. Cao, and D. Zhao, College of Computer Science and Engineering, Northeastern University, Shenyang, China; emails: [email protected], [email protected], [email protected]; A. R. Gonçalves, Lawrence Livermore National Laboratory, CA; email: [email protected]; A. Banerjee, Computing Science & Engineering, University of Minnesota, Twin Cities; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2018 ACM 1556-4681/2018/08-ART65 $15.00 https://doi.org/10.1145/3230668
Publisher Copyright:
© 2018 ACM.
PY - 2018/8
Y1 - 2018/8
N2 - Alzheimer's disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is thus an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. In this article, we propose the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the proposed model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.
AB - Alzheimer's disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is thus an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. In this article, we propose the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the proposed model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.
KW - ADMM
KW - Alzheimer's disease
KW - Disease progression
KW - Graph laplacian
KW - Multi-task learning
UR - http://www.scopus.com/inward/record.url?scp=85053481283&partnerID=8YFLogxK
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U2 - 10.1145/3230668
DO - 10.1145/3230668
M3 - Article
AN - SCOPUS:85053481283
SN - 1556-4681
VL - 12
JO - ACM Transactions on Knowledge Discovery from Data
JF - ACM Transactions on Knowledge Discovery from Data
IS - 6
M1 - 65
ER -