Towards interpretable imaging genomics analysis: Methodological developments and applications
X Cen, W Dong, W Lv, Y Zhao, F Dubee, AFA Mentis… - Information …, 2024 - Elsevier
Identifying the relationship between imaging features and genetic variation (a term coined
“imaging genomics”) offers valuable insight into the pathogenesis of cancer, as well as …
“imaging genomics”) offers valuable insight into the pathogenesis of cancer, as well as …
Contig: Self-supervised multimodal contrastive learning for medical imaging with genetics
High annotation costs are a substantial bottleneck in applying modern deep learning
architectures to clinically relevant medical use cases, substantiating the need for novel …
architectures to clinically relevant medical use cases, substantiating the need for novel …
[HTML][HTML] Multimodal analysis methods in predictive biomedicine
For medicine to fulfill its promise of personalized treatments based on a better
understanding of disease biology, computational and statistical tools must exist to analyze …
understanding of disease biology, computational and statistical tools must exist to analyze …
Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction
Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale
datasets, their use for genetic discovery remains challenging. Here we introduce an …
datasets, their use for genetic discovery remains challenging. Here we introduce an …
[HTML][HTML] Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the
visual cortex, establishing physiological connections between the eye and the brain. Despite …
visual cortex, establishing physiological connections between the eye and the brain. Despite …
iGWAS: image-based genome-wide association of self-supervised deep phenotyping of human medical images
Existing imaging genetics studies have been mostly limited in scope by using imaging-
derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self …
derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self …
Training normalizing flows from dependent data
Normalizing flows are powerful non-parametric statistical models that function as a hybrid
between density estimators and generative models. Current learning algorithms for …
between density estimators and generative models. Current learning algorithms for …
A high-fidelity inpainting method of micro-slice images based on Bendlet analysis
K Meng, M Liu, S Mei, L Yang - Biosystems Engineering, 2023 - Elsevier
Highlights•Biological slice images are useful in studying the internal structure of
organisms.•A multi-grid manifold inpainting model was proposed.•Bendlet functions system …
organisms.•A multi-grid manifold inpainting model was proposed.•Bendlet functions system …
Unsupervised representation learning improves genomic discovery for lung function and respiratory disease prediction
Background High-dimensional clinical data are becoming more accessible in biobank-scale
datasets. However, accurately phenotyping high-dimensional clinical data remains a major …
datasets. However, accurately phenotyping high-dimensional clinical data remains a major …
iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images
Existing imaging genetics studies have been mostly limited in scope by using imaging-
derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self …
derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self …