/generative-biology

Generative Biology Review

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Generative Biology Review

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Manuscript description

This manuscript is a living document to assess the growing field of generative biology -- i.e. the applications of generative AI to the life sciences. We're early in the process of writing this review, and we're even early in the process of building generative biology applications for science, engineering, and product development.

The goal of this manuscript is to collaboratively write and review the field, and will take the same form and process as the recent Opportunities and obstacles for deep learning in biology and medicine review paper. This is not a duplicative paper, as the AI world has changed dramatically in the last few months. Lets write this together and set a baseline understanding for the field.

Contribution notes

Papers should be submitted for consideration as an issue. Issues should include as much detail as possible, but at a minimum, they should include the title of the paper and a link to the paper (preferably a DOI). An ideal issue will also include a 3-5 sentence summary of the main takeaway of the paper to make it easiest to include in the relevant sections of the review.

Contributions to this manuscript are welcome in the form of pull requests. For any questions about this manuscript, submit an issue with your question, or email Alexander Titus at publications@theinvivogroup.com.

FAQ

Can I contribute?

Yes, everyone is welcome and encouraged to contribute to this manuscript. We ask that you do so through issues, pull requests, and engaging with the content thoughtfully. If we're missing a paper, create and issue and log it. If we're describing or interpreting a paper incorrectly, fork it, edit it, and create a pull request to help us be better.

Why are you writing this?

The fields of generative AI and modern biology research are moving so fast that a static review will never be up to date. This review is designed to give people a baseline place to start their journey to understand and contribute to the use of AI tools to study, design, and create new life sciences and biotechnology applications.

What do I do if I have a question?

Issues. Lots of issues. It's not that you have issues, but that we want to have issues. If you have questions, submit them so that we can track it and someone can help you. If we get the question enough, it will end up in these FAQs. Submit your issues!

What is Manubot?

Read everything on this README file below this question :-D

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. 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 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 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.

Local execution

The easiest way to run Manubot is to use continuous integration to rebuild the manuscript when the content changes. If you want to build a Manubot manuscript locally, install the conda environment as described in build. Then, you can build the manuscript on POSIX systems by running the following commands from this root directory.

# Activate the manubot conda environment (assumes conda version >= 4.4)
conda activate manubot

# Build the manuscript, saving outputs to the output directory
bash 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.
# This is required to view local images in the HTML output.

# Configure the webpage directory
manubot webpage

# You can now open the manuscript webpage/index.html in a web browser.
# Alternatively, open a local webserver at http://localhost:8000/ with the
# following commands.
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 script and manubot webpage command upon content changes:

bash build/autobuild.sh

Continuous Integration

Whenever a pull request is opened, CI (continuous integration) 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 CI logs for the cause of failure and revise your pull request accordingly.

When a commit to the main branch occurs (for example, when a pull request is merged), 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:

For continuous integration configuration details, see .github/workflows/manubot.yaml.

License

License: CC BY 4.0 License: CC0 1.0

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/in-vivo-group/generative-biology.

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.