Abstract 2059: Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets
Abstract:Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with sin… Show more
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