Another Readme tweak

README.md

@@ -3,7 +3,7 @@
 # mhcflurry
 Open source neural network models for peptide-MHC binding affinity prediction
 
-The [adaptive immune system](https://en.wikipedia.org/wiki/Adaptive_immune_system) depends on the presentation of protein fragments by [MHC](https://en.wikipedia.org/wiki/Major_histocompatibility_complex) molecules. Machine learning models of this interaction are routinely used in studies of infectious diseases, autoimmune diseases, vaccine development, and cancer immunotherapy.
+The [adaptive immune system](https://en.wikipedia.org/wiki/Adaptive_immune_system) depends on the presentation of protein fragments by [MHC](https://en.wikipedia.org/wiki/Major_histocompatibility_complex) molecules. Machine learning models of this interaction are used in studies of infectious diseases, autoimmune diseases, vaccine development, and cancer immunotherapy.
 
 MHCflurry currently supports peptide / [MHC class I](https://en.wikipedia.org/wiki/MHC_class_I) affinity prediction using one model per MHC allele. The predictors may be trained on data that has been augmented with data imputed based on other alleles (see [Rubinsteyn 2016](http://biorxiv.org/content/early/2016/06/07/054775)). We anticipate adding additional models, including pan-allele and class II predictors.
 
@@ -43,6 +43,10 @@ predict(alleles=['A0201'], peptides=['SIINFEKL'])
 0  A0201  SIINFEKL  10672.347656
 ```
 
+## Training your own models
+
+This [unit test](https://github.com/hammerlab/mhcflurry/blob/master/test/test_class1_binding_predictor_A0205.py) gives a simple example of how to train a predictor in Python. 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.
+
 ## Details on the downloaded class I allele-specific models
 
 Besides the actual model weights, the data downloaded with `mhcflurry-downloads fetch` also includes a CSV file giving the hyperparameters used for each predictor. Another CSV gives the cross validation results used to select these hyperparameters.
@@ -65,4 +69,4 @@ The path where MHCflurry looks for model weights and data can be set with the `M
 
 If you only want to change the version of the released data used, you can set `MHCFLURRY_DOWNLOADS_CURRENT_RELEASE`. If you want to change the base directory used for all releases, set `MHCFLURRY_DATA_DIR`.
 
-By default,`MHCFLURRY_DOWNLOADS_DIR` is a platform specific application storage directory, `MHCFLURRY_DOWNLOADS_CURRENT_RELEASE` is the latest release, and `MHCFLURRY_DOWNLOADS_DIR` is set to `$MHCFLURRY_DATA_DIR/$MHCFLURRY_DOWNLOADS_CURRENT_RELEASE`.
+By default, `MHCFLURRY_DOWNLOADS_DIR` is a platform specific application storage directory, `MHCFLURRY_DOWNLOADS_CURRENT_RELEASE` is the latest release, and `MHCFLURRY_DOWNLOADS_DIR` is set to `$MHCFLURRY_DATA_DIR/$MHCFLURRY_DOWNLOADS_CURRENT_RELEASE`.