diff --git a/config/config.yaml b/config/config.yaml
index 79a9912a..b87bcd45 100644
--- a/config/config.yaml
+++ b/config/config.yaml
@@ -45,13 +45,13 @@ container_registry:
 algorithms:
       - name: "pathlinker"
         params:
-              include: false
+              include: true
               run1:
                   k: range(100,201,100)
 
       - name: "omicsintegrator1"
         params:
-              include: false
+              include: true
               run1:
                   b: [5, 6]
                   w: np.linspace(0,5,2)
@@ -69,7 +69,7 @@ algorithms:
 
       - name: "meo"
         params:
-              include: false
+              include: true
               run1:
                   max_path_length: [3]
                   local_search: ["Yes"]
@@ -77,18 +77,18 @@ algorithms:
 
       - name: "mincostflow"
         params:
-              include: false
+              include: true
               run1:
                   flow: [1] # The flow must be an int
                   capacity: [1]
 
       - name: "allpairs"
         params:
-              include: false
+              include: true
 
       - name: "domino"
         params:
-              include: false
+              include: true
               run1:
                   slice_threshold: [0.3]
                   module_threshold: [0.05]
@@ -149,28 +149,28 @@ reconstruction_settings:
 analysis:
       # Create one summary per pathway file and a single summary table for all pathways for each dataset
       summary:
-        include: false
+        include: true
       # Create output files for each pathway that can be visualized with GraphSpace
       graphspace:
-        include: false
+        include: true
       # Create Cytoscape session file with all pathway graphs for each dataset
       cytoscape:
-        include: false
+        include: true
       # Machine learning analysis (e.g. clustering) of the pathway output files for each dataset
       ml:
         # ml analysis per dataset
-        include: false
+        include: true
         # adds ml analysis per algorithm output
         # only runs for algorithms with multiple parameter combinations chosen
-        aggregate_per_algorithm: false
+        aggregate_per_algorithm: true
         # specify how many principal components to calculate
         components: 2
         # boolean to show the labels on the pca graph
-        labels: false
+        labels: true
         # 'ward', 'complete', 'average', 'single'
         # if linkage: ward, must use metric: euclidean
         linkage: 'ward'
         # 'euclidean', 'manhattan', 'cosine'
         metric: 'euclidean'
       evaluation:
-        include: false
+        include: true