The explosion of high-throughput sequencing technologies has democratized genomic research, enabling investigators to generate comprehensive ‘omics datasets ...
Foundation models to bridge the data scarcity and explainability gap in pancreatic cancer diagnosis.
Integrated transcriptomic profiling and explainable machine learning to reveal functional reprogramming and biomarker candidates in pancreatic ductal adenocarcinoma.
DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images
Do you want to generate spatial transcriptomics data using your H&E images? We introduce DeepSpot, a novel deep-learning model that predicts spatial transcriptomics from H&E images. DeepSpot employs a ...
Abstract: Spatial transcriptomics (ST) enables high-resolution gene expression profiling within native tissue context, but high dropout rates and data sparsity severely impede downstream biological ...
Abstract: Spatial domain identification, a pivotal task in spatial transcriptomics (ST) research, seeks to elucidate the spatial distribution relationships among diverse cell types and complex tissue ...
Glioblastoma (GBM) exhibits marked plasticity and intense microenvironmental crosstalk. We aimed to delineate mesenchymal programs with spatial resolution, clinical relevance, and mechanistic anchors.
We implement a biologically grounded cortical circuit motif in neuromorphic hardware and AI architectures to show how experimentally informed neocortical computations, realized through ...
This manuscript presents an important contribution to the field of single-cell transcriptomic analysis in cancer by introducing a novel computational framework-SCellBOW-which applies embedding ...
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