diff --git a/backend/src/enrichment_graph.py b/backend/src/enrichment_graph.py
index 64898f34..552e127c 100644
--- a/backend/src/enrichment_graph.py
+++ b/backend/src/enrichment_graph.py
@@ -35,7 +35,6 @@ def get_functional_graph(list_enrichment, species_id):
     df2 = nodesterm.rename(columns={"id": "external_id"})
     merged = pd.merge(df2[["external_id", "fdr_rate", "p_value"]], nodes, on="external_id")
 
-    print(df2[df2.duplicated(subset=["external_id"], keep=False)].sort_values(by="external_id")["external_id"].to_list())
     # Add the two columns to df2
     nodes = merged.drop_duplicates()
 
@@ -66,7 +65,7 @@ def get_functional_graph(list_enrichment, species_id):
     nodes_sub = graph.create_nodes_subgraph(nk_graph, nodes)
 
     stopwatch.round("Enrichment")
-    
+
     if len(nodes.index) == 0:
         sigmajs_data = {"nodes": [], "edges": []}
     else:
diff --git a/backend/src/main.py b/backend/src/main.py
index 1d7e1e78..d400e6b2 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -109,7 +109,6 @@ def proteins_subgraph_api():
     # TODO: make better (Vincent)
     nodes = pd.DataFrame(proteins).rename(columns={"ENSEMBL": "external_id"}).drop_duplicates(subset="external_id")
 
-    
     edges = pd.DataFrame({"source": source, "target": target, "score": score})
     edges = edges.drop_duplicates(subset=["source", "target"])
 
diff --git a/backend/src/queries.py b/backend/src/queries.py
index 56e0b51e..2d7712a4 100644
--- a/backend/src/queries.py
+++ b/backend/src/queries.py
@@ -126,7 +126,9 @@ def get_number_of_proteins(driver: neo4j.Driver, species_id: int) -> int:
         return int(num_proteins)
 
 
-def _convert_to_connection_info_score(result: neo4j.Result, _int: bool, protein:bool) -> (list[str], list[str], list[str], list[int]):
+def _convert_to_connection_info_score(
+    result: neo4j.Result, _int: bool, protein: bool
+) -> (list[str], list[str], list[str], list[int]):
     nodes, source, target, score = list(), list(), list(), list()
 
     for row in result: