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 …
Brain imaging genomics: integrated analysis and machine learning
Brain imaging genomics is an emerging data science field, where integrated analysis of
brain imaging and genomics data, often combined with other biomarker, clinical, and …
brain imaging and genomics data, often combined with other biomarker, clinical, and …
Multi-task sparse canonical correlation analysis with application to multi-modal brain imaging genetics
Brain imaging genetics studies the genetic basis of brain structures and functionalities via
integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging …
integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging …
Associating multi-modal brain imaging phenotypes and genetic risk factors via a dirty multi-task learning method
Brain imaging genetics becomes more and more important in brain science, which
integrates genetic variations and brain structures or functions to study the genetic basis of …
integrates genetic variations and brain structures or functions to study the genetic basis of …
Map** the genetic-imaging-clinical pathway with applications to Alzheimer's disease
Alzheimer's disease is a progressive form of dementia that results in problems with memory,
thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid …
thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid …
Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease
Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the
elderly population. Early diagnosis of AD is critical for the management of this disease …
elderly population. Early diagnosis of AD is critical for the management of this disease …
The exploration of Parkinson's disease: A multi-modal data analysis of resting functional magnetic resonance imaging and gene data
X Bi, H Wu, Y **e, L Zhang, X Luo, Y Fu… - Brain Imaging and …, 2021 - Springer
Parkinson's disease (PD) is the most universal chronic degenerative neurological
dyskinesia and an important threat to elderly health. At present, the researches of PD are …
dyskinesia and an important threat to elderly health. At present, the researches of PD are …
Identifying frequency-dependent imaging genetic associations via hypergraph-structured multi-task sparse canonical correlation analysis
P Song, X Li, X Yuan, L Pang, X Song… - Computers in Biology and …, 2024 - Elsevier
Identifying complex associations between genetic variations and imaging phenotypes is a
challenging task in the research of brain imaging genetics. The previous study has proved …
challenging task in the research of brain imaging genetics. The previous study has proved …
Identifying biomarkers of Alzheimer's disease via a novel structured sparse canonical correlation analysis approach
S Wang, Y Qian, K Wei, W Kong - Journal of Molecular Neuroscience, 2022 - Springer
Using correlation analysis to study the potential connection between brain genetics and
imaging has become an effective method to understand neurodegenerative diseases …
imaging has become an effective method to understand neurodegenerative diseases …
Identifying modality-consistent and modality-specific features via label-guided multi-task sparse canonical correlation analysis for neuroimaging genetics
Brain imaging genetics provides the foundation for further revealing brain disorder, which
combines genetic variation with brain structure or functions. Recently, sparse canonical …
combines genetic variation with brain structure or functions. Recently, sparse canonical …