2 citations · 2 across the 4 of their papers we have counts for
4 papers
Latent Gene Diffusion for Spatial Transcriptomics Completion
Paula Cárdenas, Leonardo Manrique, Daniela Vega +2
Computer Vision has proven to be a powerful tool for analyzing Spatial Transcriptomics (ST) data. However, current models that predict spatially resolved gene expression from histo…
Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking
Daniela Ruiz, Paula Cárdenas, Leonardo Manrique +3
Spatial Transcriptomics is a groundbreaking technology that integrates histology images with spatially resolved gene expression profiles. Among the various Spatial Transcriptomics…
SpaRED benchmark: Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion
Gabriel Mejia, Daniela Ruiz, Paula Cárdenas +3
Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture…
SEPAL: Spatial Gene Expression Prediction from Local Graphs
Gabriel Mejia, Paula Cárdenas, Daniela Ruiz +2
Spatial transcriptomics is an emerging technology that aligns histopathology images with spatially resolved gene expression profiling. It holds the potential for understanding many…