TY - GEN
T1 - Robust and discriminative brain genome association study
AU - Zhu, Xiaofeng
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grants (AG053867, AG041721, and AG042599). X. Zhu was supported in part by the National Natural Science Foundation of China (61876046 and 61573270), the Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents, the Strategic Research Excellence Fund at Massey University, and the Marsden Fund of New Zealand (MAU1721). The authors thank the Alzheimer’s Disease Neuroimaging Initiative for providing the data sets.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Brain Genome Association (BGA) study, which investigates the associations between brain structure/function (characterized by neuroimaging phenotypes) and genetic variations (characterized by Single Nucleotide Polymorphisms (SNPs)), is important in pathological analysis of neurological disease. However, the current BGA studies are limited as they did not explicitly consider the disease labels, source importance, and sample importance in their formulations. We address these issues by proposing a robust and discriminative BGA formulation. Specifically, we learn two transformation matrices for mapping two heterogeneous data sources (i.e., neuroimaging data and genetic data) into a common space, so that the samples from the same subject (but different sources) are close to each other, and also the samples with different labels are separable. In addition, we add a sparsity constraint on the transformation matrices to enable feature selection on both data sources. Furthermore, both sample importance and source importance are also considered in the formulation via adaptive parameter-free sample and source weightings. We have conducted various experiments, using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, to test how well the neuroimaging phenotypes and SNPs can represent each other in the common space.
AB - Brain Genome Association (BGA) study, which investigates the associations between brain structure/function (characterized by neuroimaging phenotypes) and genetic variations (characterized by Single Nucleotide Polymorphisms (SNPs)), is important in pathological analysis of neurological disease. However, the current BGA studies are limited as they did not explicitly consider the disease labels, source importance, and sample importance in their formulations. We address these issues by proposing a robust and discriminative BGA formulation. Specifically, we learn two transformation matrices for mapping two heterogeneous data sources (i.e., neuroimaging data and genetic data) into a common space, so that the samples from the same subject (but different sources) are close to each other, and also the samples with different labels are separable. In addition, we add a sparsity constraint on the transformation matrices to enable feature selection on both data sources. Furthermore, both sample importance and source importance are also considered in the formulation via adaptive parameter-free sample and source weightings. We have conducted various experiments, using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, to test how well the neuroimaging phenotypes and SNPs can represent each other in the common space.
UR - http://www.scopus.com/inward/record.url?scp=85075688825&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_50
DO - 10.1007/978-3-030-32251-9_50
M3 - Conference contribution
AN - SCOPUS:85075688825
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 456
EP - 464
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -