# HG changeset patch
# User immuneml
# Date 1649153512 0
# Node ID 051d349fdc8c5e3c5a350b6fd6ed5cef7cc8cea9
# Parent  697c2b98a4666958ba28b64579f9bfbd064831a7
"planemo upload commit 5ffe9db26c26d30c923c812b69346d95948e9cd0"

diff -r 697c2b98a466 -r 051d349fdc8c immuneml_train_ml_model.xml
--- a/immuneml_train_ml_model.xml	Thu Mar 17 16:38:38 2022 +0000
+++ b/immuneml_train_ml_model.xml	Tue Apr 05 10:11:52 2022 +0000
@@ -62,8 +62,7 @@
         contains the output of the TrainMLModel instruction including all trained models and their predictions, and report results.
         Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
 
-      - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding, and
-        optionally preprocessing steps). This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
+      - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding). This .zip file can subsequently be used as an input when applying previously trained ML models to a new dataset. Currently, this can only be done locally using the command-line interface.
 
     ]]>
 
diff -r 697c2b98a466 -r 051d349fdc8c immuneml_train_recept.xml
--- a/immuneml_train_recept.xml	Thu Mar 17 16:38:38 2022 +0000
+++ b/immuneml_train_recept.xml	Tue Apr 05 10:11:52 2022 +0000
@@ -186,8 +186,7 @@
           contains the output of the TrainMLModel instruction including all trained models and their predictions, and report results.
           Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
 
-        - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding).
-          This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
+        - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding). This .zip file can subsequently be used as an input when applying previously trained ML models to a new dataset. Currently, this can only be done locally using the command-line interface.
 
         - receptor_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
           downloaded, altered, and run again by immuneML using the  `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
diff -r 697c2b98a466 -r 051d349fdc8c immuneml_train_repert.xml
--- a/immuneml_train_repert.xml	Thu Mar 17 16:38:38 2022 +0000
+++ b/immuneml_train_repert.xml	Tue Apr 05 10:11:52 2022 +0000
@@ -215,8 +215,7 @@
           contains the output of the TrainMLModel instruction including all trained models and their predictions, and report results.
           Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
 
-        - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding).
-          This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
+        - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding). This .zip file can subsequently be used as an input when applying previously trained ML models to a new dataset. Currently, this can only be done locally using the command-line interface.
 
         - repertoire_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
           downloaded, altered, and run again by immuneML using the  `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.