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iMIRACLE: an Iterative Multi-View Graph Neural Network to Model Intercellular Gene Regulation from Spatial Transcriptomic Data

Spatial transcriptomics has transformed genomic research by measuring spatially resolved gene expressions, allowing us to investigate how cells adapt to their microenvironment via modulating their expressed genes. This essential process usually …

Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation

Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions …

scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding

Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While …

ExAD-GNN: explainable graph neural network for Alzheimer’s disease state prediction from single-cell data

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder with significant impacts on patients and their families. Therefore, accurate and early diagnosis of AD is crucial for improving patient outcomes and developing effective treatments. …

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing …