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Structured Sparse Low-Rank Regression Model for Brain-Wide and Genome-Wide Associations
- Source :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 ISBN: 9783319467191, MICCAI (1)
- Publication Year :
- 2016
- Publisher :
- Springer International Publishing, 2016.
-
Abstract
- With the advances of neuroimaging techniques and genome sequences understanding, the phenotype and genotype data have been utilized to study the brain diseases (known as imaging genetics). One of the most important topics in image genetics is to discover the genetic basis of phenotypic markers and their associations. In such studies, the linear regression models have been playing an important role by providing interpretable results. However, due to their modeling characteristics, it is limited to effectively utilize inherent information among the phenotypes and genotypes, which are helpful for better understanding their associations. In this work, we propose a structured sparse low-rank regression method to explicitly consider the correlations within the imaging phenotypes and the genotypes simultaneously for Brain-Wide and Genome-Wide Association (BW-GWA) study. Specifically, we impose the low-rank constraint as well as the structured sparse constraint on both phenotypes and phenotypes. By using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we conducted experiments of predicting the phenotype data from genotype data and achieved performance improvement by 12.75 % on average in terms of the root-mean-square error over the state-of-the-art methods.
- Subjects :
- 0301 basic medicine
Genotype
Imaging genetics
Genome-wide association study
Computational biology
Biology
Polymorphism, Single Nucleotide
Sensitivity and Specificity
Genome
Article
03 medical and health sciences
0302 clinical medicine
Neuroimaging
Alzheimer Disease
Humans
business.industry
Reproducibility of Results
Pattern recognition
Regression analysis
Regression
Constraint (information theory)
Phenotype
030104 developmental biology
Regression Analysis
Artificial intelligence
Canonical correlation
business
Algorithms
030217 neurology & neurosurgery
Genome-Wide Association Study
Subjects
Details
- ISBN :
- 978-3-319-46719-1
- ISBNs :
- 9783319467191
- Database :
- OpenAIRE
- Journal :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 ISBN: 9783319467191, MICCAI (1)
- Accession number :
- edsair.doi.dedup.....ed604b4bd4f030628e11ec09485cf5fc