Comments (17)
@agitter thanks! How about the following citation conventions:
- Always use a DOI for the version of record if available. Cite DOIs using
[@doi:10.15363/thinklab.4]
- If no DOI for the version of record, use a PubMed ID. For example,
[@pmid:26158728]
. - If the article is an arxiv preprint, use
[@arxiv:1508.06576]
. - If the article has none of the above, big problem. File an issue.
You can do multiple citations using: [@doi:10.15363/thinklab.4 @pmid:26158728 @arxiv:1508.06576]
from deep-review.
Also wanted to tag @YosephBarash and @davek44 who have been active in this area for their thoughts.
from deep-review.
What is the format of the text itself? GitHub markdown?
from deep-review.
Does this need to actually have anything to do with "systems pharmacology"?
from deep-review.
@michaelmhoffman It should probably roughly touch on topics that could be construed as systems pharmacology. My read is that the precision medicine perspective + deep learning on genomic/transcriptomic/proteomic/etc data gets us close enough.
Format of the text itself will be markdown [eventually I'll convert it to LaTeX and reformat]. I think we will use something like [@doi:doi_link] for citations. @dhimmel has code to automatically pull down doi metadata and covert to bibtex.
from deep-review.
Are there any approaches/data types that have taken off in other fields but that are under-utilized here?
Reinforcement learning perhaps? http://karpathy.github.io/2016/05/31/rl/ gives a brief intro. I'm not aware of examples in biology or medicine.
from deep-review.
Reinforcement learning hooked up to some experimental system would be fun.
from deep-review.
@dhimmel has code to automatically pull down doi metadata and covert to bibtex.
@cgreene, the code is here. Let me know when formatting time comes and I can help with the auto-conversion of citations.
from deep-review.
@dhimmel that looks great. May want to pair it with Arxiv2bib at formatting time.
from deep-review.
@dhimmel proposed citation conventions look good to me. Do you want to file a PR to add it to the contribution instructions?
from deep-review.
@cgreene there are a lot of Imaging + Bio deep learning papers out there. Should we take a more targeted approach for logging them as issues, such as focusing on those that pertain to human disease and medicine, instead of trying to catalog everything? What might be the main points of this subsection?
from deep-review.
@agitter : I guess I'd say, if one could make an argument that it's relevant to our current guiding question [which I think still needs a bit of refinement - but probably an increase in specificity, not a decrease] then those are the ones for which we should file an issue.
from deep-review.
Going to close this now that the discussion has been captured in subsequent issues.
from deep-review.
@dhimmel This issue was closed, but I wanted to ask a follow up question now that we're starting to write. The citation conventions above will be great for making the citations machine-readable for automated bibliography construction. Do you have any ideas for how to make them human-readable as well? For example, in latex/bibtex I might use \cite{Zhou2015_deep_sea}
so that anyone reviewing my text knows which paper I'm discussing. That will be harder to do when reviewing DOIs and PMIDs.
from deep-review.
Do you have any ideas for how to make them human-readable as well?
DOIs have some semantic meaning (often contain a journal abbreviation). But we could define another category such as [tag:Zhou2015_deep_sea]
and then you'd also have to update a mapping file where Zhou2015_deep_sea
would point to a valid machine-readable citation. Do you think that is a good solution?
from deep-review.
@dhimmel yes, I think that would be the best solution for machine- and human-readable citations. It adds overhead for the authors so we'll have to weigh those tradeoffs.
@cgreene do you have an opinion about creating a citation mapping file?
from deep-review.
I defer to you and Daniel.
On Thu, Oct 27, 2016, 8:14 AM Anthony Gitter [email protected]
wrote:
@dhimmel https://github.com/dhimmel yes, I think that would be the best
solution for machine- and human-readable citations. It adds overhead for
the authors so we'll have to weigh those tradeoffs.@cgreene https://github.com/cgreene do you have an opinion about
creating a citation mapping file?—
You are receiving this because you were mentioned.Reply to this email directly, view it on GitHub
#2 (comment),
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAhHsyxt2LfDNMD2AVezGXxetFqEDvd8ks5q4JWkgaJpZM4Jb6zp
.
from deep-review.
Related Issues (20)
- Potential Reorganization HOT 7
- Deep Learning for the Life Sciences
- Deepprune: Learning Efficient and Interpretable Convolutional Networks Through Weight Pruning for Predicting DNA-Protein Binding
- Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets
- In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
- Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
- Update subsection: Structure-based prediction of bioactivity HOT 2
- Update 'Imaging applications in healthcare'
- Applications in sleep research HOT 2
- LISA: Towards Learned DNA Sequence Search HOT 1
- DeepArk: modeling cis-regulatory codes of model species with deep learning
- Human Protein Atlas (HPA) Cell Type Prediction using Deep Learning and estimating uncertainty HOT 2
- How can one cite this dynamic review? HOT 3
- Update for 2020? HOT 3
- Add a Systematic Text-Mining Component to the Review? HOT 3
- Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs HOT 3
- Alpha Fold 2 HOT 1
- feature and labels HOT 1
- Update in Proteomics mass spectrometry HOT 2
- Consider change to reference 346 HOT 9
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from deep-review.