A proof of concept for using Manubot to automate awesome lists.
An awesome list is a themed list of resources in the README.md file in the master branch of a GitHub repository.
In this repository, README.md is created via continuous integration and should not be edited directly.
Edit README-BASE.md to update this text.
Update the reference lists in the content directory to add new sections or references.
This is only a proof of concept that is not robust against errors in the scripts or merge conflicts.
The .travis.yml, deploy.sh, and environment.yml files were derived from https://github.com/manubot/rootstock (CC0 1.0 license).
This repository also contains a GitHub Actions workflow that uses Manubot to automatically extract reference information from the identifier in an issue title.
The workflow only runs on issues with the label reference.
See #7 for an example.
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Open collaborative writing with Manubot
Daniel S. Himmelstein, Vincent Rubinetti, David R. Slochower, Dongbo Hu, Venkat S. Malladi, Casey S. Greene, Anthony Gitter
(2019-07-09) https://greenelab.github.io/meta-review/ -
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, … Casey S. Greene
Journal of The Royal Society Interface (2018-04-04) https://doi.org/gddkhn
DOI: 10.1098/rsif.2017.0387 · PMID: 29618526 · PMCID: PMC5938574 -
Python utilities for Manubot: Manuscripts, open and automated: manubot/manubot
Manubot
(2019-07-18) https://github.com/manubot/manubot
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Machine-learning-guided directed evolution for protein engineering
Kevin K. Yang, Zachary Wu, Frances H. Arnold
Nature Methods (2019-07-15) https://doi.org/gf43h4
DOI: 10.1038/s41592-019-0496-6 · PMID: 31308553 -
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue
arXiv (2019-04-17) https://arxiv.org/abs/1904.08102v1 -
Unified rational protein engineering with sequence-only deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George M. Church
Cold Spring Harbor Laboratory (2019-03-26) https://doi.org/gf48g2
DOI: 10.1101/589333 -
Navigating the protein fitness landscape with Gaussian processes
P. A. Romero, A. Krause, F. H. Arnold
Proceedings of the National Academy of Sciences (2012-12-31) https://doi.org/f4k8bz
DOI: 10.1073/pnas.1215251110 · PMID: 23277561 · PMCID: PMC3549130
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A comparison of single-cell trajectory inference methods
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, Yvan Saeys
Nature Biotechnology (2019-04-01) https://doi.org/gfxsgd
DOI: 10.1038/s41587-019-0071-9 · PMID: 30936559 -
An overview of algorithms for estimating pseudotime in single-cell RNA-seq data: agitter/single-cell-pseudotime
Anthony Gitter
(2019-07-19) https://github.com/agitter/single-cell-pseudotime -
Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data
Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter
Cold Spring Harbor Laboratory (2019-01-30) https://doi.org/gft4bb
DOI: 10.1101/534834