Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2017 - Elsevier
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued
development and standardization of methodologies for biomarkers and has provided an …
development and standardization of methodologies for biomarkers and has provided an …
Machine learning for brain imaging genomics methods: a review
In the past decade, multimodal neuroimaging and genomic techniques have been
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …
Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers
In this paper, we propose a novel sparse regression method for Brain-Wide and Genome-
Wide association study. Specifically, we impose a low-rank constraint on the weight …
Wide association study. Specifically, we impose a low-rank constraint on the weight …
[图书][B] Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019 …
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the
refereed proceedings of the 22nd International Conference on Medical Image Computing …
refereed proceedings of the 22nd International Conference on Medical Image Computing …
A hierarchical feature and sample selection framework and its application for Alzheimer's disease diagnosis
Classification is one of the most important tasks in machine learning. Due to feature
redundancy or outliers in samples, using all available data for training a classifier may be …
redundancy or outliers in samples, using all available data for training a classifier may be …
Semi-supervised hierarchical multimodal feature and sample selection for Alzheimer's disease diagnosis
Alzheimer's disease (AD) is a progressive neurodegenerative disease that impairs a
patient's memory and other important mental functions. In this paper, we leverage the …
patient's memory and other important mental functions. In this paper, we leverage the …
Structured sparse low-rank regression model for brain-wide and genome-wide associations
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 …
phenotype and genotype data have been utilized to study the brain diseases (known as …
Identifying candidate genetic associations with MRI-derived AD-related ROI via tree-guided sparse learning
Imaging genetics has attracted significant interests in recent studies. Traditional work has
focused on mass-univariate statistical approaches that identify important single nucleotide …
focused on mass-univariate statistical approaches that identify important single nucleotide …
Strategies for integrated analysis in imaging genetics studies
Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual,
deepening our knowledge of the biological mechanisms behind neurodevelopmental …
deepening our knowledge of the biological mechanisms behind neurodevelopmental …
Identifying genetic risk factors for Alzheimer's disease via shared tree-guided feature learning across multiple tasks
The genome-wide association study (GWAS) is a popular approach to identify disease-
associated genetic factors for Alzhemer's Disease (AD). However, it remains challenging …
associated genetic factors for Alzhemer's Disease (AD). However, it remains challenging …