# HG changeset patch
# User bgruening
# Date 1710319683 0
# Node ID e5d28e255f5860b80ac68fac95f3029e7bd85775
# Parent  e66700f4c7ebf1d5d29df607db834fafa70d072a
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit fe5cbcff4786c445b47538472ca4b4a099d2f652

diff -r e66700f4c7eb -r e5d28e255f58 biomodels_BIOMD0000001066.xml
--- a/biomodels_BIOMD0000001066.xml	Tue Mar 12 17:03:04 2024 +0000
+++ b/biomodels_BIOMD0000001066.xml	Wed Mar 13 08:48:03 2024 +0000
@@ -41,36 +41,36 @@
         </configfile>
     </configfiles>
     <inputs>
-        <param name="input_file" type="data" label="Test data" format="tabular,csv,xlsx" multiple="false"/>
+	<param name="input_file" type="data" label="Test data" format="tabular,csv,xlsx" multiple="false" />
     </inputs>
     <outputs>
-        <data format="tabular" name="output_file" label="Predicted data"/>
+        <data format="tabular" name="output_file" label="Predicted data"></data>
     </outputs>
     <tests>
         <test>
-            <param name="input_file" value="test_data.tabular" ftype="tabular"/>
+            <param name="input_file" value="test_data.tabular" ftype="tabular" />
             <output name="output_file" file="pred_data.tabular">
                 <assert_contents>
-                    <has_n_columns n="17"/>
-                    <has_n_lines n="296"/>
+                    <has_n_columns n="17" />
+                    <has_n_lines n="296" />
                 </assert_contents>
             </output>
         </test>
         <test>
-            <param name="input_file" value="test_data.csv" ftype="csv"/>
+            <param name="input_file" value="test_data.csv" ftype="csv" />
             <output name="output_file" file="pred_data.tabular">
                 <assert_contents>
-                    <has_n_columns n="17"/>
-                    <has_n_lines n="296"/>
+                    <has_n_columns n="17" />
+                    <has_n_lines n="296" />
                 </assert_contents>
             </output>
         </test>
         <test>
-            <param name="input_file" value="test_data.xlsx" ftype="xlsx"/>
+            <param name="input_file" value="test_data.xlsx" ftype="xlsx" />
             <output name="output_file" file="pred_data.tabular">
                 <assert_contents>
-                    <has_n_columns n="17"/>
-                    <has_n_lines n="296"/>
+                    <has_n_columns n="17" />
+                    <has_n_lines n="296" />
                 </assert_contents>
             </output>
         </test>
diff -r e66700f4c7eb -r e5d28e255f58 biomodels_BIOMD0000001076.xml
--- a/biomodels_BIOMD0000001076.xml	Tue Mar 12 17:03:04 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,93 +0,0 @@
-<tool id="biomodels_BIOMD0000001076" name="Prediction of Breast Cancer (BC)" version="@VERSION@" profile="23.05">
-    <description>proteins involved in cancer immunotherapy using molecular descriptors and Multi Layer Perceptron (MLP) neural network</description>
-    <macros>
-        <token name="@VERSION@">1</token>
-    </macros>
-    <creator>
-        <organization name="EMBL's European Bioinformatics Institute (EMBL-EBI)" url="https://www.ebi.ac.uk/biomodels/" email="biomodels-net-support@lists.sf.net"/>
-        <person name="Anup Kumar" email="kumara@informatik.uni-freiburg.de"/>
-    </creator>
-    <requirements>
-        <container type="docker">docker.io/anupkumar/biomodels_biomd0000001076:@VERSION@</container>
-    </requirements>
-    <command><![CDATA[
-    cp /home/\$NB_USER/MLP_model3.onnx ./ &&
-    python '$biom_script_BIOMD0000001076'
-]]>
-    </command>
-    <configfiles>
-        <configfile name="biom_script_BIOMD0000001076">
-            <![CDATA[
-import os
-import numpy
-import onnxruntime as rt
-import pandas as pd
-
-#if $input_file.ext == "tabular":
-data_test = pd.read_csv('$input_file', sep="\t")
-#elif $input_file.ext == "csv":
-data_test = pd.read_csv('$input_file', sep=",")
-#end if
-
-Xdatap = data_test.iloc[:,:-1].values
-sess = rt.InferenceSession(os.getcwd() + "/MLP_model3.onnx")
-input_name = sess.get_inputs()[0].name
-label_name = sess.get_outputs()[0].name
-pred_onx = sess.run([label_name], {input_name: Xdatap.astype(numpy.float32)})[0]
-data_test["Predictions"] = pred_onx
-data_test.to_csv('$output_file', sep="\t", index=None)
-
-]]>
-        </configfile>
-    </configfiles>
-    <inputs>
-        <param name="input_file" type="data" label="Test data" format="tabular,csv" multiple="false"/>
-    </inputs>
-    <outputs>
-        <data format="tabular" name="output_file" label="Predicted data"/>
-    </outputs>
-    <tests>
-        <test>
-            <param name="input_file" value="test_data_BIOMD0000001076.tabular" ftype="tabular"/>
-            <output name="output_file" file="pred_data_BIOMD0000001076.tabular">
-                <assert_contents>
-                    <has_n_columns n="302"/>
-                    <has_n_lines n="401"/>
-                </assert_contents>
-            </output>
-        </test>
-        <test>
-            <param name="input_file" value="test_data_BIOMD0000001076.csv" ftype="csv"/>
-            <output name="output_file" file="pred_data_BIOMD0000001076.tabular">
-                <assert_contents>
-                    <has_n_columns n="302"/>
-                    <has_n_lines n="401"/>
-                </assert_contents>
-            </output>
-        </test>
-    </tests>
-    <help>
-        <![CDATA[
-**What it does**
-
-The tool makes prediction of breast cancer-related proteins: non-breast cancer related protein (0) or related to breast cancer protein (1). 300 features are used to train a Multi Layer Perceptron (artificial neural network) for such a prediction. The model is available via a Docker container. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.
-
-
-**Description**
-
-This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting. Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein.
-
-**Input file**
-
-Provide a tabular file (as tabular or csv) containing all the features as mentioned above.
-
-**Output file**
-
-Returns predicted data as a tabular file - the predictions are concatenated as a last column of the test data.
-
-        ]]>
-    </help>
-    <citations>
-        <citation type="doi">10.1038/s41598-020-65584-y</citation>
-    </citations>
-</tool>