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Open source peptide/MHC I binding affinity prediction. Competitive accuracy, fast,
and [documented](http://www.hammerlab.org/mhcflurry/).
[](https://travis-ci.org/hammerlab/mhcflurry) [](https://coveralls.io/github/hammerlab/mhcflurry?branch=master)
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MHCflurry supports Class I peptide/MHC binding affinity prediction using
ensembles of allele-specific models. You can fit MHCflurry models to your own data
or download models that we fit to data from
[IEDB](http://www.iedb.org/home_v3.php) and [Kim 2014](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241).
MHCflurry supports Python versions 2.7 and 3.4+. It uses the [keras](https://keras.io)
neural network library via either the Tensorflow or Theano backends. GPUs may
optionally be used for a generally modest speed improvement.
If you find MHCflurry useful in your research please cite:
> O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC binding affinity prediction. bioRxiv. Available at: http://www.biorxiv.org/content/early/2017/08/09/174243.
## Installation (pip)
Install the package:
```
$ pip install mhcflurry
```
Then download our datasets and trained models:
```
$ mhcflurry-downloads fetch
```
You can now generate predictions:
```
$ mhcflurry-predict \
--alleles HLA-A0201 HLA-A0301 \
--peptides SIINFEKL SIINFEKD SIINFEKQ \
--out /tmp/predictions.csv \
Wrote: /tmp/predictions.csv
```
See the [documentation](http://www.hammerlab.org/mhcflurry/) for more details.