fix comments etc.

README.md

@@ -473,19 +473,20 @@ Follow these steps exactly in the order provided:
 python neo4j_etl.py
 ```
 
-  **Eingabe:** Das Skript wird Sie nacheinander nach Ihrem **Datenbank-Usernamen** (Standard: `neo4j`) und Ihrem **Passwort** fragen.
+  **Input:** The script will ask you for your **database-username** (default: `neo4j`) and your **password**.
-  **Verarbeitung:** Das Skript liest automatisch alle Abfragen aus dem Verzeichnis `neo4jqueries/analysis_queries` aus.
+  **Processing:** The script automatically reads and executes cypher queries in the following directory `neo4jqueries/analysis_queries`.
-  **Ausgabe:** Die Ergebnisse der Analyse-Queries werden direkt in der Konsole ausgegeben.
+  **Output:** Results of the analysis will be displayed on the terminal.
 
 ---
 
-### Projektstruktur
+### Structure
 
-| Verzeichnis / Datei                 | Funktion                                                   |
+| Directory / file                    | Functionality                                          |
-| :---------------------------------- | :--------------------------------------------------------- |
+| :---------------------------------- | :----------------------------------------------------- |
-| `neo4j_etl.py`                      | Das Python-Skript zur Ausführung der Analyse-Queries.      |
+| `neo4j_etl.py`                      | Python-Script for execting analysis queries.           |
-| `neo4jqueries/loadingQueriesNeo4j/` | Enthält alle Cypher-Dateien für den initialen Datenimport. |
+| `neo4jqueries/loadingQueriesNeo4j/` | Contains all Cypher files for the initial data import. |
-| `neo4jqueries/analysis_queries/`    | Enthält Cypher-Dateien für die statistische Auswertung.    |
+| `neo4jqueries/analysis_queries/`    | Includes Cypher files for  analysis.                   |
+|                                     |                                                        |
 
 ---
 

dashboard.py

@@ -104,7 +104,7 @@ try:
             else:
                 st.sidebar.warning("No drugs found")
     
-    # OVERVIEW PAGE
+    # overview page
     if page == "Overview":
         st.header("Dataset Overview")
         
@@ -114,7 +114,7 @@ try:
         col3.metric("Repurposing Opportunities", f"{len(repurposing):,}")
         col4.metric("Analyzed Drugs", f"{len(super_drugs):,}")
         
-        # STATISTICS BOXES
+        # statistics boxes
         st.markdown("---")
         st.subheader("Key Statistics")
         
@@ -163,7 +163,7 @@ try:
                 xaxis_title="Gene",
                 yaxis_title="Number of Diseases"
             )
-            # Enable chart export
+            # enable chart export
             config = {'displayModeBar': True, 'displaylogo': False}
             st.plotly_chart(fig, use_container_width=True, config=config)
             
@@ -185,7 +185,7 @@ try:
             csv = top_diseases.to_csv(index=False).encode('utf-8')
             st.download_button("Download Data", csv, "top_diseases.csv", "text/csv")
     
-    # HOTSPOT GENES PAGE
+    # hotspot gene page
     elif page == "Hotspot Genes":
         st.header("Hotspot Genes - Most Disease Associations")
         
@@ -215,7 +215,7 @@ try:
         csv = filtered_genes.to_csv(index=False).encode('utf-8')
         st.download_button("Download Filtered Data", csv, "hotspot_genes.csv", "text/csv")
     
-    # DRUG REPURPOSING PAGE
+    # drug repurposing page
     elif page == "Drug Repurposing":
         st.header("Drug Repurposing Opportunities")
         
@@ -250,7 +250,7 @@ try:
             csv = filtered.to_csv(index=False).encode('utf-8')
             st.download_button("Download", csv, f"repurposing_{selected}.csv", "text/csv")
     
-    # POLYPHARMACY RISK PAGE
+    # polypharmacy risk page
     elif page == "Polypharmacy Risk":
         st.header("Polypharmacy Risk Analysis")
         
@@ -285,7 +285,7 @@ try:
         csv = filtered_risk.to_csv(index=False).encode('utf-8')
         st.download_button("Download Risk Data", csv, "polypharmacy_risk.csv", "text/csv")
     
-    # SYMPTOM TRIANGLE PAGE
+    # symptop triangle page
     elif page == "Symptom Triangle":
         st.header("Symptom-Disease-Drug Connections")
         
@@ -314,7 +314,7 @@ try:
         csv = top_symptoms.to_csv(index=False).encode('utf-8')
         st.download_button("Download Symptom Data", csv, "symptom_triangle.csv", "text/csv")
     
-    # SUPER DRUGS PAGE
+    # super drugs page
     elif page == "Super Drugs":
         st.header("Super-Drug Score (Best Benefit/Risk Ratio)")
         
@@ -352,7 +352,7 @@ try:
         csv = filtered_super.to_csv(index=False).encode('utf-8')
         st.download_button("Download Super Drugs", csv, "super_drugs.csv", "text/csv")
     
-    # DRUG CONFLICTS PAGE
+    # drug conflicts page
     elif page == "Drug Conflicts":
         st.header("Drug Conflicts - Overlapping Side Effects")
         
@@ -394,28 +394,28 @@ try:
         else:
             st.warning("Drug conflicts data not available. Run the ETL script to generate this analysis.")
     
-    # NETWORK GRAPH PAGE
+    # network graph page
     elif page == "Network Graph":
         st.header("Disease-Gene-Drug Network")
         
         if network_nodes is not None and network_edges is not None:
             st.info("Interactive network visualization showing connections between diseases, genes, and drugs")
             
-            # Create networkx graph
+            # create networkx graph
             G = nx.Graph()
             
-            # Add nodes
+            # add nodes
             for _, row in network_nodes.iterrows():
                 G.add_node(row['id'], label=row['label'], type=row['type'])
             
-            # Add edges
+            # add edges
             for _, row in network_edges.iterrows():
                 G.add_edge(row['source'], row['target'])
             
-            # Create layout
+            # create layout
             pos = nx.spring_layout(G, k=0.5, iterations=50)
-            
+        
-            # Create edge trace
+            # create edge trace
             edge_x = []
             edge_y = []
             for edge in G.edges():
@@ -431,7 +431,7 @@ try:
                 mode='lines'
             )
             
-            # Create node traces (separate by type for legend)
+            # create node traces (separate by type for legend)
             node_traces = []
             color_map = {
                 'Disease': '#ff4444',
@@ -466,7 +466,7 @@ try:
                     )
                     node_traces.append(node_trace)
             
-            # Create figure
+            # create figure
             fig = go.Figure(data=[edge_trace] + node_traces,
                           layout=go.Layout(
                               title='Disease-Gene-Drug Network',
@@ -488,7 +488,7 @@ try:
         else:
             st.warning("Network data not available. Run the ETL script to generate this visualization.")
     
-    # COMPARE DRUGS PAGE
+    # compare drugs page
     elif page == "Compare Drugs":
         st.header("⚖️ Compare Drugs Side-by-Side")
         
@@ -522,7 +522,7 @@ try:
                 st.metric("Side Effects", int(drug2_data['num_side_effects']))
                 st.metric("Super Score", f"{drug2_data['super_score']:.2f}")
             
-            # Comparison chart
+            # comparison chart
             comparison_df = pd.DataFrame({
                 'Metric': ['Diseases Treated', 'Side Effects', 'Super Score'],
                 drug1: [drug1_data['num_diseases_treated'], drug1_data['num_side_effects'], drug1_data['super_score']],
@@ -539,7 +539,7 @@ try:
             config = {'displayModeBar': True, 'displaylogo': False}
             st.plotly_chart(fig, use_container_width=True, config=config)
             
-            # Winner determination
+            # winner determination
             st.markdown("---")
             st.subheader("Recommendation")
             

etl.py

@@ -4,7 +4,7 @@ from pathlib import Path
 from collections import defaultdict
 
 
-# KONFIGURATION
+# config
 
 INPUT_JSON = "hetionet-v1.0.json"
 OUTPUT_DIR = Path("neo4j_csv")
@@ -14,7 +14,7 @@ print("="*60)
 print("HETIONET ETL PIPELINE (OPTIMIZED + SPLIT EDGES)")
 print("="*60)
 
-# EXTRACT
+# extract
 
 print("\nPHASE 1: EXTRACTION")
 print("-"*60)
@@ -29,7 +29,7 @@ edges_raw = data["edges"]
 print(f"Nodes loaded: {len(nodes_raw):,}")
 print(f"Edges loaded: {len(edges_raw):,}")
 
-# TRANSFORM – NODES
+# transfomr – nodes
 
 print("\nPHASE 2: TRANSFORM NODES")
 print("-"*60)
@@ -48,7 +48,7 @@ for node in nodes_raw:
 
 nodes_df = pd.DataFrame(nodes_flat)
 
-# Spaltennamen Neo4j-sicher machen
+# make column names neo4j safe
 nodes_df.columns = (
     nodes_df.columns
     .str.replace(" ", "_")
@@ -59,11 +59,11 @@ nodes_df.columns = (
 print(f"Processed {len(nodes_df):,} nodes")
 print(f"   Columns: {', '.join(nodes_df.columns[:5])}...")
 
-# Create lookup dictionaries
+# create lookup dictionaries
 node_id_to_kind = dict(zip(nodes_df['id'], nodes_df['kind']))
 node_id_to_name = dict(zip(nodes_df['id'], nodes_df['name']))
 
-# Create sets for fast membership testing
+# create sets for fast membership testing
 gene_ids = set(nodes_df[nodes_df['kind'] == 'Gene']['id'])
 disease_ids = set(nodes_df[nodes_df['kind'] == 'Disease']['id'])
 symptom_ids = set(nodes_df[nodes_df['kind'] == 'Symptom']['id'])
@@ -77,7 +77,7 @@ print(f"   - Symptoms: {len(symptom_ids):,}")
 print(f"   - Compounds: {len(compound_ids):,}")
 print(f"   - Side Effects: {len(sideeffect_ids):,}")
 
-# Export nodes by type
+# export nodes by type
 print("\nExporting node files...")
 for kind in nodes_df["kind"].unique():
     df_kind = (
@@ -89,7 +89,7 @@ for kind in nodes_df["kind"].unique():
     df_kind.to_csv(filename, index=False)
     print(f"   {filename.name} ({len(df_kind):,} rows)")
 
-# TRANSFORM – EDGES
+# transform edges
 
 print("\nPHASE 3: TRANSFORM EDGES")
 print("-"*60)
@@ -107,7 +107,7 @@ for i, edge in enumerate(edges_raw):
 
 edges_df = pd.DataFrame(edges)
 
-# Relationship-Typen Neo4j-sicher machen
+# make relationship types neo4j safe
 edges_df["type"] = edges_df["type"].str.replace(" ", "_").str.replace("-", "_")
 
 # split edges into seperate files
@@ -120,13 +120,13 @@ for edge_type in sorted(edge_types):
     edges_subset = edges_df[edges_df['type'] == edge_type]
     filename = OUTPUT_DIR / f"edges_{edge_type}.csv"
     
-    # Only export source and target (type is in filename)
+    # only export source and target (type is in filename)
     edges_subset[['source', 'target']].to_csv(filename, index=False)
     
     size_mb = filename.stat().st_size / (1024*1024)
     print(f"   ✓ edges_{edge_type:20s}.csv ({len(edges_subset):>10,} rows, {size_mb:>6.2f} MB)")
 
-# Also keep the combined file for backward compatibility
+# also keep the combined file for backward compatibility
 edges_file = OUTPUT_DIR / "edges_all.csv"
 edges_df.to_csv(edges_file, index=False)
 print(f"\n   ✓ edges_all.csv (combined) ({len(edges_df):,} rows)")
@@ -136,7 +136,7 @@ print(f"   Total edges: {len(edges_df):,}")
 print(f"   Split into {len(edge_types)} separate CSV files")
 print(f"   Each file can be loaded independently!")
 
-# Pre-filter edges by type for analysis
+# pre-filter edges by type for analysis
 print("\nEdge type distribution:")
 edges_by_type = {}
 for edge_type in sorted(edge_types):
@@ -145,14 +145,12 @@ for edge_type in sorted(edge_types):
     pct = 100 * count / len(edges_df)
     print(f"   - {edge_type:20s}: {count:>10,} ({pct:>5.1f}%)")
 
-# [ANALYSES - keeping all the existing analysis code...]
-# (Keeping the same analysis code as before)
 
 print("\n" + "="*60)
 print("PHASE 4: ANALYSES")
 print("="*60)
 
-# ANALYSIS 1: HOTSPOT GENES
+# analysis 1: hotspot genes
 print("\nAnalysis 1: Hotspot Genes")
 print("-"*60)
 
@@ -183,7 +181,7 @@ genes_df_sorted.to_csv(OUTPUT_DIR / "nodes_Gene.csv", index=False)
 
 print(f"Top gene: {genes_df_sorted.iloc[0]['name']} ({int(genes_df_sorted.iloc[0]['num_diseases'])} diseases)")
 
-# ANALYSIS 2: DISEASE SYMPTOM DIVERSITY
+# analysis 2: disease symptom diversity
 print("\nAnalysis 2: Disease Symptom Diversity")
 print("-"*60)
 
@@ -208,7 +206,7 @@ disease_df_sorted.to_csv(OUTPUT_DIR / "nodes_Disease.csv", index=False)
 
 print(f"Top disease: {disease_df_sorted.iloc[0]['name']} ({int(disease_df_sorted.iloc[0]['num_symptoms'])} symptoms)")
 
-# Build indices for drug analyses
+# build indices for drug analyses
 print("\nBuilding indices for drug analyses...")
 disease_to_genes = defaultdict(set)
 gene_to_diseases = defaultdict(set)
@@ -238,7 +236,7 @@ print(f"\n💡 For faster Neo4j loading, use the split edge files:")
 print(f"   edges_associates.csv, edges_treats.csv, etc.")
 print(f"   Instead of the combined edges_all.csv")
 
-# ANALYSIS 3: DRUG REPURPOSING
+# analysis 3: drug repurposing
 print("\nAnalysis 3: Drug Repurposing Opportunities")
 print("-"*60)
 
@@ -273,7 +271,7 @@ if len(repurposing_df) > 0:
     repurposing_df.to_csv(OUTPUT_DIR / "analysis_drug_repurposing.csv", index=False)
     print(f"Found {len(repurposing_df):,} repurposing opportunities")
 
-# ANALYSIS 4: POLYPHARMACY RISK
+# analysis 4: polypharmacy risk
 print("\nAnalysis 4: Polypharmacy Risk")
 print("-"*60)
 
@@ -294,7 +292,7 @@ if len(drug_sideeffects) > 0:
     drug_risk_sorted.to_csv(OUTPUT_DIR / "analysis_polypharmacy_risk.csv", index=False)
     print(f"Analyzed {len(drug_risk_sorted):,} drugs for side effects")
 
-# ANALYSIS 5: SYMPTOM TRIANGLE
+# analysis 5: symptom triangle
 print("\nAnalysis 5: Symptom-Disease-Drug Triangle")
 print("-"*60)
 
@@ -326,7 +324,7 @@ if len(symptom_triangle_df) > 0:
     symptom_triangle_df.to_csv(OUTPUT_DIR / "analysis_symptom_triangle.csv", index=False)
     print(f"Analyzed {len(symptom_triangle_df):,} symptoms")
 
-# ANALYSIS 6: SUPER DRUGS
+# analysis 6: super drugs
 print("\nAnalysis 6: Super-Drug Score")
 print("-"*60)
 
@@ -352,7 +350,7 @@ if len(super_drugs_df) > 0:
     super_drugs_df.to_csv(OUTPUT_DIR / "analysis_super_drugs.csv", index=False)
     print(f"Analyzed {len(super_drugs_df):,} drugs")
 
-# ANALYSIS 7: DRUG CONFLICTS
+# analysis 7: drug conflicts
 print("\nAnalysis 7: Drug Conflicts")
 print("-"*60)
 
@@ -390,7 +388,7 @@ if len(drug_conflicts_df) > 0:
     drug_conflicts_df.to_csv(OUTPUT_DIR / "analysis_drug_conflicts.csv", index=False)
     print(f"Found {len(drug_conflicts_df):,} drug conflict pairs")
 
-# ANALYSIS 8: NETWORK DATA
+# analysis 8: network data
 print("\nAnalysis 8: Network Visualization Data")
 print("-"*60)
 
@@ -401,7 +399,7 @@ network_edges = []
 node_id_counter = 0
 id_mapping = {}
 
-# Add disease nodes
+# add disease nodes
 for disease_id in top_diseases:
     node_id = f"d_{node_id_counter}"
     id_mapping[disease_id] = node_id
@@ -413,7 +411,7 @@ for disease_id in top_diseases:
     })
     node_id_counter += 1
 
-# Add genes
+# add genes
 disease_genes = gene_disease_edges[
     gene_disease_edges['source'].isin(top_diseases)
 ].head(150)
@@ -437,7 +435,7 @@ for _, row in disease_genes.iterrows():
         'type': 'associates'
     })
 
-# Add drugs
+# add drugs
 drug_treatments = treats_edges[treats_edges['target'].isin(top_diseases)].head(50)
 
 for _, row in drug_treatments.iterrows():

neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Associates copy.cypher

@@ -1,4 +1,4 @@
-LOAD CSV WITH HEADERS FROM 'file:///edges_treats.csv' AS row
+LOAD CSV WITH HEADERS FROM 'file:///edges_associates.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
-CREATE (source)-[:TREATS]->(target);
+CREATE (source)-[:ASSOCIATES]->(target);

neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Upregulates.cypher

@@ -1,4 +1,4 @@
-LOAD CSV WITH HEADERS FROM 'file:///edges_causes.csv' AS row
+LOAD CSV WITH HEADERS FROM 'file:///edges_upregulates.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
-CREATE (source)-[:CAUSES]->(target);
+CREATE (source)-[:UPREGULATES]->(target);