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
L Shen, PM Thompson - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
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 …
Multimodal data analysis of Alzheimer's disease based on clustering evolutionary random forest
X Bi, X Hu, H Wu, Y Wang - IEEE Journal of Biomedical and …, 2020 - ieeexplore.ieee.org
Alzheimer's disease (AD) has become a severe medical challenge. Advances in
technologies produced high-dimensional data of different modalities including functional …
technologies produced high-dimensional data of different modalities including functional …
A review of fusion methods for omics and imaging data
The development of omics data and biomedical images has greatly advanced the progress
of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and …
of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and …
Ensemble manifold regularized multi-modal graph convolutional network for cognitive ability prediction
Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make
predictions about individual behavioral and cognitive traits based on brain connectivity …
predictions about individual behavioral and cognitive traits based on brain connectivity …
[HTML][HTML] The Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain
JM Stephen, I Solis, J Janowich, M Stern, MR Frenzel… - NeuroImage, 2021 - Elsevier
Brain development has largely been studied through unimodal analysis of neuroimaging
data, providing independent results for structural and functional data. However, structure …
data, providing independent results for structural and functional data. However, structure …
A manifold regularized multi-task learning model for IQ prediction from two fMRI paradigms
Objective: Multi-modal brain functional connectivity (FC) data have shown great potential for
providing insights into individual variations in behavioral and cognitive traits. The joint …
providing insights into individual variations in behavioral and cognitive traits. The joint …
Detecting risk gene and pathogenic brain region in EMCI using a novel GERF algorithm based on brain imaging and genetic data
X Bi, W Zhou, L Li, Z Xing - IEEE Journal Of Biomedical and …, 2021 - ieeexplore.ieee.org
Fusion analysis of disease-related multi-modal data is becoming increasingly important to
illuminate the pathogenesis of complex brain diseases. However, owing to the small amount …
illuminate the pathogenesis of complex brain diseases. However, owing to the small amount …
Latent similarity identifies important functional connections for phenotype prediction
Objective: Endophenotypes such as brain age and fluid intelligence are important
biomarkers of disease status. However, brain imaging studies to identify these biomarkers …
biomarkers of disease status. However, brain imaging studies to identify these biomarkers …
Multiview diffusion map improves prediction of fluid intelligence with two paradigms of fmri analysis
Objective: To understand the association between brain networks and behaviors of an
individual, most studies build predictive models based on functional connectivity (FC) from a …
individual, most studies build predictive models based on functional connectivity (FC) from a …