Deep learning for bioimage analysis in developmental biology
Deep learning has transformed the way large and complex image datasets can be
processed, resha** what is possible in bioimage analysis. As the complexity and size of …
processed, resha** what is possible in bioimage analysis. As the complexity and size of …
Computational methods for single-cell imaging and omics data integration
Integrating single cell omics and single cell imaging allows for a more effective
characterisation of the underlying mechanisms that drive a phenotype at the tissue level …
characterisation of the underlying mechanisms that drive a phenotype at the tissue level …
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 …
Multi-domain translation between single-cell imaging and sequencing data using autoencoders
The development of single-cell methods for capturing different data modalities including
imaging and sequencing has revolutionized our ability to identify heterogeneous cell states …
imaging and sequencing has revolutionized our ability to identify heterogeneous cell states …
High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations
Cells can be perturbed by various chemical and genetic treatments and the impact on gene
expression and morphology can be measured via transcriptomic profiling and image-based …
expression and morphology can be measured via transcriptomic profiling and image-based …
transferGWAS: GWAS of images using deep transfer learning
Motivation Medical images can provide rich information about diseases and their biology.
However, investigating their association with genetic variation requires non-standard …
However, investigating their association with genetic variation requires non-standard …
Joint analysis of expression levels and histological images identifies genes associated with tissue morphology
Histopathological images are used to characterize complex phenotypes such as tumor
stage. Our goal is to associate features of stained tissue images with high-dimensional …
stage. Our goal is to associate features of stained tissue images with high-dimensional …
Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data
Accurate prognosis for cancer patients can provide critical information for optimizing
treatment plans and improving life quality. Combining omics data and demographic/clinical …
treatment plans and improving life quality. Combining omics data and demographic/clinical …
L0-sparse canonical correlation analysis
Canonical Correlation Analysis (CCA) models are powerful for studying the associations
between two sets of variables. The canonically correlated representations, termed\textit …
between two sets of variables. The canonically correlated representations, termed\textit …
Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Background Current methods for analyzing single-cell datasets have relied primarily on
static gene expression measurements to characterize the molecular state of individual cells …
static gene expression measurements to characterize the molecular state of individual cells …