Ten Quick Tips for Deep Learning in Biology
This is the manuscript for a community-written article aimed at PLOS Computational Biology. Deep learning is exploding in popularity and is increasingly finding its way into biological data analysis. However, since most biologists receive little (or no) data science training, using deep learning properly can be a daunting task.
We're going to try to fix that. By boiling down the community's knowledge into ten quick tips, we hope to increase the number of biological researchers using DL (by making it more inviting) and the quality of the research (by helping them avoid common mistakes).
Where we are now
Currently, we're in the process of drafting the paper with each tip being tracked in its own project. The journal has already approved our presubmission inquiry, so we are ready to write! To contribute to the paper, take a look at
CONTRIBUTING.md for information on how we're organizing the writing as well as
USAGE.md for instructions on how to write the actual text.
How you can help
Help of any kind is highly appreciated. Want to pitch in but not sure how?
- Help us write the tips into a manuscript.
- Know of anyone who might be interested? Send them an email!
- Have Twitter followers? Tweet about the project!
A note on the project's name
Originally, this paper was going to be titled "Ten simple rules for deep learning in biology." However, the journal asked that we change the name to "n quick tips for deep learning in biology" to be placed into the educational section as well as to give us more latitude with the number of tips we have. While we changed the name of the paper, for simplicity, we kept the name of the repo as
Manubot is a system for writing scholarly manuscripts via GitHub.
Manubot automates citations and references, versions manuscripts using git, and enables collaborative writing via GitHub.
An overview manuscript presents the benefits of collaborative writing with Manubot and its unique features.
The rootstock repository is a general purpose template for creating new Manubot instances, as detailed in
USAGE.md for documentation how to write a manuscript.
Please open an issue for questions related to Manubot usage, bug reports, or general inquiries.
Repository directories & files
The directories are as follows:
contentcontains the manuscript source, which includes markdown files as well as inputs for citations and references. See
USAGE.mdfor more information.
outputcontains the outputs (generated files) from the manubot including the resulting manuscripts. You should not edit these files manually, because they will get overwritten.
webpageis a directory meant to be rendered as a static webpage for viewing the HTML manuscript.
buildcontains commands and tools for building the manuscript.
cicontains files necessary for deployment via continuous integration. For the CI configuration, see
# Activate the manubot conda environment (assumes conda version >= 4.4) conda activate manubot # Build the manuscript, saving outputs to the output directory sh build/build.sh # At this point, the HTML & PDF outputs will have been created. The remaining # commands are for serving the webpage to view the HTML manuscript locally. # Configure the webpage directory python build/webpage.py # View the manuscript locally at http://localhost:8000/ cd webpage python -m http.server
Sometimes it's helpful to monitor the content directory and automatically rebuild the manuscript when a change is detected.
The following command, while running, will trigger both the
webpage.py scripts upon content changes:
Whenever a pull request is opened, Travis CI will test whether the changes break the build process to generate a formatted manuscript. The build process aims to detect common errors, such as invalid citations. If your pull request build fails, see the Travis CI logs for the cause of failure and revise your pull request accordingly.
When a commit to the
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gh-pages branch uses GitHub Pages to host the following URLs:
- HTML manuscript at https://Benjamin-Lee.github.io/deep-rules/
- PDF manuscript at https://Benjamin-Lee.github.io/deep-rules/manuscript.pdf
For continuous integration configuration details, see
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LICENSE.md), which allows reuse with attribution.
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