We recognized that deep learning in precision medicine is a rapidly developing area. Hence, diverse expertise was required to provide a forward-looking perspective. Accordingly, we collaboratively wrote this review in the open, enabling anyone with expertise to contribute. We wrote the manuscript in markdown and tracked changes using git. Contributions were handled through GitHub, with individuals submitting "pull requests" to suggest additions to the manuscript.
To facilitate citation, we defined a markdown citation syntax. We supported citations to the following identifier types (in order of preference): DOIs, PubMed Central IDs, PubMed IDs, arXiv IDs, and URLs. References were automatically generated from citation metadata by querying APIs to generate Citation Style Language (CSL) JSON items for each reference. Pandoc and pandoc-citeproc converted the markdown to HTML and PDF, while rendering the formatted citations and references. In total, referenced works consisted of {{manuscript_stats.reference_counts.doi}} DOIs, {{manuscript_stats.reference_counts.pmcid}} PubMed Central records, {{manuscript_stats.reference_counts.arxiv}} arXiv manuscripts, and {{manuscript_stats.reference_counts.url}} URLs (webpages as well as manuscripts lacking standardized identifiers).
We implemented continuous analysis so the manuscript was automatically regenerated whenever the source changed [@doi:10.1038/nbt.3780].
We configured Travis CI---a continuous integration service---to fetch new citation metadata and rebuild the manuscript for every commit.
Accordingly, formatting or citation errors in pull requests would cause the Travis CI build to fail, automating quality control.
In addition, the build process renders templated variables, such as the reference counts mentioned above, to automate the updating of dynamic content.
When contributions were merged into the master branch, Travis CI deployed the built manuscript by committing back to the GitHub repository.
As a result, the latest manuscript version is always available at https://greenelab.github.io/deep-review.
To ensure a consistent software environment, we defined a versioned conda environment of the software dependencies.
In addition, we instructed the Travis CI deployment script to perform blockchain timestamping [@url:https://www.bgcarlisle.com/blog/2014/08/25/proof-of-prespecified-endpoints-in-medical-research-with-the-bitcoin-blockchain/; @url:http://blog.dhimmel.com/irreproducible-timestamps/]. Using OpenTimestamps, we submitted hashes for the manuscript and the source git commit for timestamping in the Bitcoin blockchain [@url:https://opentimestamps.org/]. These timestamps attest that a given version of this manuscript (and its history) existed at a given point in time. The ability to irrefutably prove manuscript existence at a past time could be important to establish scientific precedence and enforce an immutable record of authorship.
We created an open repository on the GitHub version control platform (greenelab/deep-review) [@url:https://github.com/greenelab/deep-review].
Here, we engaged with numerous authors from papers within and outside of the area.
The manuscript was drafted via GitHub commits by {{authors | length}} individuals who met the ICMJE standards of authorship.
These were individuals who contributed to the review of the literature;
drafted the manuscript or provided substantial critical revisions;
approved the final manuscript draft; and agreed to be accountable in all aspects of the work.
Individuals who did not contribute in all of these ways, but who did participate, are acknowledged below.
We grouped authors into the following four classes of approximately equal contributions and randomly ordered authors within each contribution class.
Drafted multiple sub-sections along with extensive editing, pull request reviews, or discussion: A.A.K., B.K.B., B.T.D., D.S.H., E.F., G.P.W., M.M.H., M.Z., P.A., T.C.
Drafted one or more sub-sections: A.E.C., A.M.A., A.S., B.J.L., C.A.L., E.M.C., G.L.R., J.I., J.L., J.X., S.C.T., S.W., W.X., Z.L.
Revised specific sub-sections or supervised drafting one or more sub-sections: A.H., A.K., D.D., D.J.H., L.K.W., M.H.S.S., S.J.S., S.M.B., Y.P., Y.Q.
Drafted sub-sections, edited the manuscript, reviewed pull requests, and coordinated co-authors: A.G., C.S.G.
A.K. is on the Advisory Board of Deep Genomics Inc. E.F. is a full-time employee of GlaxoSmithKline. The remaining authors have no competing interests to declare.
We gratefully acknowledge Christof Angermueller, Kumardeep Chaudhary, Gökcen Eraslan, Mikael Huss, Bharath Ramsundar and Xun Zhu for their discussion of the manuscript and reviewed papers on GitHub. We would like to thank Aaron Sheldon, who contributed text but did not formally approve the manuscript. We would like to thank Anna Greene for a careful proofreading of the manuscript in advance of the first submission. We would like to thank Robert Gieseke, Ruibang Luo, Sourav Singh, and GitHub user snikumbh for correcting typos, formatting, and references.
We acknowledge funding from the Gordon and Betty Moore Foundation awards GBMF4552 (C.S.G. and D.S.H.) and GBMF4563 (D.J.H.); the Howard Hughes Medical Institute (S.C.T.); the National Institutes of Health awards DP2GM123485 (A.K.), P30CA051008 (S.M.B.), R01AI116794 (B.K.B.), R01GM089652 (A.E.C.), R01GM089753 (J.X.), R01LM012222 (S.J.S.), R01LM012482 (S.J.S.), R21CA220398 (S.M.B.), T32GM007753 (B.T.D.), T32HG000046 (G.P.W.), and U54AI117924 (A.G.); the National Institutes of Health Intramural Research Program and National Library of Medicine (Y.P. and Z.L.); the National Science Foundation awards 1245632 (G.L.R.), 1531594 (E.M.C.), and 1564955 (J.X.); the Natural Sciences and Engineering Research Council of Canada award RGPIN-2015-3948 (M.M.H.); and the Roy and Diana Vagelos Scholars Program in the Molecular Life Sciences (M.Z.).