Software repository for CytoCensus, formerly QBrain, machine learning software for identifying cells in 3-D tissue.
MS ID#: BIORXIV/2017/137406
MS TITLE: CytoCensus: mapping cell identity and division in tissues and organs using machine learning
Authors: Martin Hailstone, Dominic Waithe, Tamsin J Samuels, Lu Yang, Ita Costello, Yoav Arava, Elizabeth J Robertson, Richard M Parton, Ilan Davis
https://www.biorxiv.org/content/10.1101/137406v4
The latest compiled releases can be found here: https://github.com/hailstonem/CytoCensus/releases/ Simply download the version for your operating system and run the software directly.
If you are a capable Python(3) user then you can clone the above repository and run from source. Using git:
git clone https://github.com/hailstonem/CytoCensus.git
Clone the repository, install requirements using pip:
pip install -r requirements.txt
or create a new environment with conda:
conda env create --file environment.yml
conda activate cytocensus
Run "python v2_release.py" for the training interface to be loaded. Run "python v2_evaluate.py" to load the interface for the bulk running of files.
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Q: Where do I download CytoCensus from?
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Q: How do I use CytoCensus?
A: A manual is included in the download
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Q: The software is slow to load
A: It should only be slow the first time you run it on a particular computer. Subsequent usage should be much faster.
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Q: Which executable should I run first?
A: The train software should be run first as this software allows you to train the software and produce models which can than be applied in bulk. The bulk software allows you to apply your previously trained model to one or more datasets.