@@ -8,16 +8,15 @@ The [adaptive immune system](https://en.wikipedia.org/wiki/Adaptive_immune_syste
MHCflurry currently supports allele-specific peptide / [MHC class I](https://en.wikipedia.org/wiki/MHC_class_I) affinity prediction using two approaches:
* Ensembles of predictors trained on random halves of the training data (the default)
* Single-model predictors for each allele trained on all the training data
* Single-model predictors for each allele trained on all data
For both kinds of predictors, you can fit models to your own data or download
trained models that we provide.
The downloadable models were trained on data from
[IEDB](http://www.iedb.org/home_v3.php) and [Kim 2014]
In validation experiments using presented peptides identified by mass-spec,
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@@ -27,11 +26,11 @@ available soon.
We anticipate adding additional models, including pan-allele and class II predictors.
The MHCflurry predictors are implemented in Python using [keras](https://keras.io).
## Setup
To configure keras, the neural network library used by MHCflurry, you'll need to set an environment variable in your shell:
The MHCflurry predictors are implemented in Python using [keras](https://keras.io).
To configure keras you'll need to set an environment variable in your shell:
```
export KERAS_BACKEND=theano
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@@ -91,28 +90,27 @@ The predictions returned by `predict` are affinities (KD) in nM.
## Training your own models
See the [class1_allele_specific_models.ipynb](https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb) notebook for an overview of the Python API, including predicting, fitting, and scoring models.
See the [class1_allele_specific_models.ipynb](https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb) notebook for an overview of the Python API, including predicting, fitting, and scoring single-model predictors. There is also a script called `mhcflurry-class1-allele-specific-cv-and-train` that will perform cross validation and model selection given a CSV file of training data. Try `mhcflurry-class1-allele-specific-cv-and-train --help` for details.
There is also a script called `mhcflurry-class1-allele-specific-cv-and-train` that will perform cross validation and model selection given a CSV file of training data. Try`mhcflurry-class1-allele-specific-cv-and-train --help` for details.
The ensemble predictors are trained similarly using the`mhcflurry-class1-allele-specific-ensemble-train` command.
## Details on the downloadable models
The scripts we use to train predictors, including hyperparameter selection
for the ensemble predictors and [here](downloads-generation/models_class1_allele_specific_single)
for the single-model predictors.
For the ensemble predictors, we also generate a [report](http://htmlpreview.github.io/?https://github.com/hammerlab/mhcflurry/blob/master/downloads-generation/models_class1_allele_specific_ensemble/models-summary/report.html)
that describes the hyperparamters selected and the test performance of each
that describes the hyperparameters selected and the test performance of each
model.
Besides the model weights, the data downloaded when you run
`mhcflurry-downloads fetch` also includes a CSV file giving the
hyperparameters used for each predictor. Run `mhcflurry-downloads path
models_class1_allele_specific_ensemble` or `mhcflurry-downloads path
models_class1_allele_specific_single` to get the directory to look in for these
files.
models_class1_allele_specific_single` to get the directory where these files are stored.