Fusing Modalities byMultiplexed Graph Neural Networks forOutcome Prediction inTuberculosisIn a complex disease such as tuberculosis, the evidence for the disease and its evolution may be present in multiple modalities such as clinical, genomic, or imaging data. Effective patient-tailored outcome ...
neurons that encode a feature, the width of the edges indicates the number of neurons that co-encode a behavioral feature. The network is shown in the Kamada–Kawai projection86(the distance between nodes approximate their graph-theoretical distance), with additional text labels on the network on...
Mapping the localization of multiple proteins in their native three-dimensional (3D) context would be useful across many areas of biomedicine, but multiplexed fluorescence imaging has limited intrinsic multiplexing capability, and most methods for increa
The graph shows highly enriched biological processes among the human N-glycoproteins (colored in red). The node size is according to the number of proteins corresponding to each biological process, and the intensity of the color is according to the p value. The developed SRM assays are accessibl...
Predictive modelling of highly multiplexed tumour tissue images by graph neural networksThe progression and treatment response of cancer largely depends on the complex tissue structure that surrounds cancer cells in a tumour, known as the tumour microenvironment (TME). Recent technical......
Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by...
To obtain CSLs, a k-nearest neighbor graph (k = 15) of cVAE latent representations of the subsampled data was computed and partitioned with the Leiden algorithm13 using a resolution of 0.5 (0.9 for the HeLa dataset). For comparison, the subsampled multiplexed pixel profiles were also ...
The cells were then clustered by first building a k-nearest-neighbor graph with 15 neighbors (using cosine distance) and then clustered using Louvain’s community detection implemented in the igraph package with default parameters24. Processing and analysis of CosMX data Data corresponding to the ...
Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks. Nat. Commun. 12, 1–12 (2021). Article ADS CAS Google Scholar Brown, J. K., Pemberton, A. D., Wright, S. H. & Miller, H. R. P. Primary antibody-Fab fragment...
Coreograph was newly developed for MCMICRO and has not been published elsewhere. It is implemented for the first time in MCMICRO. Its function is to split, or ‘dearray,’ a stitched TMA image into separate image stacks per core. It employs a semantic segmentation preprocessing step to assi...