Code Monkey home page Code Monkey logo

neoplasmer's Introduction

DOI

neoplasmer (ALPHA RELEASE)

Matches names of cancers and cancer-related conditions to ontology classes

Usage (via Docker)

Currently the recommended way to do this is on the command line via the neoplasmer.sh bash script:

./neoplasmer.sh tests/data/vicc_input.txt

You do not need to install anything other than Docker. The shell script is standalone.

The input file is a newline delimited list of terms. See tests/data/ for examples.

The first execution will take a minute or two; some warning messages may be printed, these can be ignored. Two directories will be created: RDF-Cache and .cache

Subsequent executions will be much faster

You can see example results in scratch/vicc-results.tsv

Python client

First, start a service:

./neoplasmer.sh --port 9055

Then install python libs:

pip3 install -r requirements.txt

Run the test client:

python3 bin/neoplasmer-client.py 'lung cancer' 'brain glioma'

TODO: additional documentation on how to this works

Running without docker

Install SWI-Prolog from http://www.swi-prolog.org

Run directly using bin/neoplasmer

neoplasmer's People

Contributors

cmungall avatar

Stargazers

avatar avatar avatar avatar

Watchers

avatar avatar avatar

neoplasmer's Issues

Plug into VICC pipelines

Instructions from @ahwagner below - we want to better automate this to have a workflow or services we can run to run this regularly over VICC unnormalized.

This was done by using the same harvester routines we use in production, at https://github.com/ohsu-comp-bio/g2p-aggregator/tree/v0.12/harvester.

Specifically, the `harvest` and `convert` phases were run using the utility scripts `harvest-file-all.sh` and `convert-file-all.sh` here: https://github.com/ohsu-comp-bio/g2p-aggregator/tree/v0.12/util.

Finally, I extracted the relevant terms from the pre-normalized `.convert.json` files using jq:

`cat *.convert.json | jq '.association.phenotypes | .[]?.description' | sort -u > unnormalized_disease_terms.txt`

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo 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.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.