@@ -14,13 +14,13 @@ ensembles of allele-specific models. You can fit MHCflurry models to your own da
[IEDB](http://www.iedb.org/home_v3.php) and [Kim 2014](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241).
Our combined dataset is available for download [here](https://github.com/hammerlab/mhcflurry/releases/download/pre-1.0.0-alpha/data_curated.tar.bz2).
We are working on a performance comparison of these models with other predictors
such as netMHCpan, which we plan to make available soon.
Pan-allelic prediction is supported in principle but is not yet performing
accurately. Infrastructure for modeling other aspects of antigen
processing is also implemented but experimental.
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.
## Setup
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@@ -105,4 +105,4 @@ predictions of the individual models. The training script is [here](downloads-ge
## Environment variables
The path where MHCflurry looks for model weights and data can be set with the `MHCFLURRY_DOWNLOADS_DIR` environment variable. This directory should contain subdirectories like "models_class1".
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The path where MHCflurry looks for model weights and data can be set with the `MHCFLURRY_DOWNLOADS_DIR` environment variable. This directory should contain subdirectories like "models_class1".