diff immuneml_train_recept.xml @ 6:2d3dd9ff7e84 draft

"planemo upload commit 74f2bd15d2b7723c8e5a22d743913706dc7d8333-dirty"

--- a/immuneml_train_recept.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_train_recept.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -98,7 +98,7 @@
         Alternatively, if you want to predict a property per immune repertoire, such as disease status, check out the
         `Train immune repertoire classifiers (simplified interface) <https://galaxy.immuneml.uio.no/root?tool_id=novice_immuneml_interface>`_ tool instead.
 
-        The full documentation can be found `here <https://docs.immuneml.uio.no/galaxy/galaxy_simple_receptors.html>`_.
+        The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_receptors.html>`_.
 
         **Basic terminology**
 
@@ -112,7 +112,7 @@
         determines how we should represent our data to the ML model. This representation is called **encoding**. In this tool, the encoding is automatically chosen based on
         the user's assumptions about the dataset.
 
-        .. image:: https://docs.immuneml.uio.no/_images/receptor_classification_overview.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/receptor_classification_overview.png
             :height: 500
 
         |
@@ -137,7 +137,7 @@
         A graphical representation of how a CDR3 sequence can be divided into k-mers, and how these k-mers can relate to specific positions in a 3D immune receptor
         (here: antibody) is shown in this figure:
 
-        .. image:: https://docs.immuneml.uio.no/_images/3mer_to_3d.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/3mer_to_3d.png
             :height: 250
 
         |
@@ -187,7 +187,7 @@
           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/galaxy/galaxy_apply_ml_models.html>`_.
+          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>`_.
 
         - 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 <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.