MUSE is a deep learning approach characterizing tissue composition through combined analysis of morphologies and transcriptional states for spatially resolved transcriptomics data.
data analysis codes for manuscript "Analysis of growth cone extension in standardized coordinates highlights self-organization rules during wiring of the Drosophila visual system"
Code and data of the "Multi-domain adversarial learning" paper, Schoenauer-Sebag et al., accepted at ICLR 2019
Code for MAGS project
Code for paper
Code for the paper "PHOCOS: Inferring Multi-Feature Phenotypic Crosstalk Networks " in Bioinformatics 2016
An easy to use, image analysis software package that enables rapid exploration and interpretation of microscopy data.
An open-source framework to synthetically generate fluorescent microscopic images of cellular population.
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