CODEX, a neural network approach to explore signaling dynamics landscapes

Published: 1 February 2021| Version 2 | DOI: 10.17632/4vnndy59fp.2
Contributors:
,
, Maciej Dobrzynski

Description

Supplementary data and R/Python code required to reproduce the figures from the accompanying publication: Jacques et al. "CODEX, a neural network approach to explore signaling dynamics landscapes". The pre-print available here: https://doi.org/10.1101/2020.08.05.237842 Included datasets: * Single-cell ERK/Akt activity dynamics in responses to GF treatment. Acquired in-house by Paolo Gagliardi. * p53 responses to ionizing radiation. Data kindly provided by Galit Lahav and Jacob Ornstein-Stewart. * SMAD2 response to TGFb. Data kindly provided by Alexander Loewer. * Drosophila speed movement. Data obtained from https://github.com/benfulcher/hctsa_phenotypingFly * Synthetic data

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Steps to reproduce

To view this repository, download everything, unpack all zip files and open the index.html file in the root directory. The file contains a browsable HTML content with a detailed description.

Institutions

Universitat Bern

Categories

Molecular Biology, Machine Learning, Clustering, Fluorescence Microscopy, Pattern Recognition in Bioinformatics, Time Series, Computational Biology

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