hhy5277 / deep-rules

Ten Quick Tips for Deep Learning in Biology

Ten Quick Tips for Deep Learning in Biology

Manuscript description

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).

Join us!

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.

The raw text of the manuscript is stored within the content directory. The rendered manuscript may be viewed as HTML here or as a PDF here.

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!

For more information, see the contribution guidelines. All contributions are subject to the code of conduct.

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 deep-rules.

Manubot

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. The rootstock repository is a general purpose template for creating new Manubot instances, as detailed in SETUP.md. See 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:

• content contains the manuscript source, which includes markdown files as well as inputs for citations and references. See USAGE.md for more information.
• output contains the outputs (generated files) from the manubot including the resulting manuscripts. You should not edit these files manually, because they will get overwritten.
• webpage is a directory meant to be rendered as a static webpage for viewing the HTML manuscript.
• build contains commands and tools for building the manuscript.
• ci contains files necessary for deployment via continuous integration. For the CI configuration, see .travis.yml.

Local execution

To run the Manubot locally, install the conda environment as described in build. Then, you can build the manuscript on POSIX systems by running the following commands.

# 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 build.sh and webpage.py scripts upon content changes:

sh build/autobuild.sh

Continuous Integration

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 master branch occurs (for example, when a pull request is merged), Travis CI builds the manuscript and writes the results to the gh-pages and output branches. The 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 .travis.yml.

License

Except when noted otherwise, the entirety of this repository is licensed under a CC BY 4.0 License (LICENSE.md), which allows reuse with attribution. Please attribute by linking to https://github.com/Benjamin-Lee/deep-rules.

Since CC BY is not ideal for code and data, certain repository components are also released under the CC0 1.0 public domain dedication (LICENSE-CC0.md). All files matched by the following glob patterns are dual licensed under CC BY 4.0 and CC0 1.0:

• *.sh
• *.py
• *.yml / *.yaml
• *.json
• *.bib
• *.tsv
• .gitignore

All other files are only available under CC BY 4.0, including:

• *.md
• *.html
• *.pdf
• *.docx

Please open an issue for any question related to licensing.