finishes setup

2026-02-10 17:43:26 +01:00
parent 4f1a5c311f
commit 3003310be0
39 changed files with 2251611 additions and 1188 deletions
--- a/17
+++ b/17
@@ -0,0 +1,17 @@
 FROM python:3.11-slim
 ENV PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1 \
    PIP_NO_CACHE_DIR=1
 WORKDIR /app
 COPY . /app
 RUN pip install --upgrade pip \
    && pip install streamlit pandas plotly networkx neo4j
 EXPOSE 8501
 CMD ["bash", "-c", "python etl.py || true && exec streamlit run dashboard.py --server.port=8501 --server.address=0.0.0.0"]
--- a/README.md
+++ b/README.md
@@ -8,13 +8,13 @@ A comprehensive ETL pipeline and interactive dashboard for analyzing biomedical
 - [Features](#features)
 - [Prerequisites](#prerequisites)
 - [Installation](#installation)
 - [Usage](#usage)
 - [Project Structure](#project-structure)
 - [Data Analyses](#data-analyses)
 - [Dashboard Features](#dashboard-features)
 - [Output Files](#output-files)
 - [Technical Details](#technical-details)
-
+- [Neo4j ETL & Analysis Pipeline](#neo4j-etl--analysis-pipeline)
 ## Overview
 This project implements a complete data pipeline for the Hetionet knowledge graph, consisting of:
@@ -82,7 +82,7 @@ networkx>=3.0
 ## Installation
-### 1. Clone or Download Project Files
+### Clone or Download Project Files
 ```bash
 # Create project directory
@@ -90,7 +90,48 @@ mkdir hetionet_analysis
 cd hetionet_analysis
 ```
-### 2. Set Up Python Environment
+### (Optional) Docker Setup for Dashboard
 You can run the Streamlit dashboard in a Docker container for easier deployment.
 Dockerfile example (already present in project root)
 ```bash
 FROM python:3.11-slim
 ENV PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1 \
    PIP_NO_CACHE_DIR=1
 WORKDIR /app
 COPY . /app
 RUN pip install --upgrade pip \
    && pip install streamlit pandas plotly networkx neo4j
 EXPOSE 8501
 CMD ["bash", "-c", "python etl.py || true && exec streamlit run dashboard.py --server.port=8501 --server.address=0.0.0.0"]
 ```
 Build Docker image
 ```bash
 docker build -t etl-dashboard .
 ```
 Run etl and dashboard in Docker
 ```bash
 docker run -p 8501:8501 etl-dashboard
 ```
 ## Non Docker usage
 ### 1. Set Up Python Environment
 ```bash
 # Create virtual environment
@@ -106,34 +147,21 @@ source etl_projekt/bin/activate
 pip install pandas streamlit plotly networkx
 ```
-### 3. Download Hetionet Data
+### 2. Run ETL Pipeline
 Download `hetionet-v1.0.json` from [Hetionet GitHub](https://github.com/hetio/hetionet) and place it in the project directory.
 ### 4. Add Project Files
 Place the following files in your project directory:
 - `hetionet_etl_final.py` - Main ETL script
 - `dashboard.py` - Streamlit dashboard
 ## Usage
 ### Step 1: Run ETL Pipeline
 Execute the ETL pipeline to process the Hetionet data:
 ```bash
-python hetionet_etl_final.py
+python etl.py
 ```
-**Expected Runtime**: 1-2 minutes
+**Expected Runtime**: ~ 1 minute
-**Output**: Creates `neo4j_csv/` directory with 20 CSV files
+**Output**: Creates `neo4j_csv/` directory with CSV files
-### Step 2: Launch Dashboard
+### 3. Launch Dashboard
-Start the interactive dashboard:
+Directly with Python
 ```bash
 streamlit run dashboard.py
@@ -141,7 +169,7 @@ streamlit run dashboard.py
 The dashboard will automatically open in your web browser at `http://localhost:8501`
-### Step 3: Explore Data
+## Explore Data
 Navigate through the dashboard using the sidebar menu:
@@ -165,6 +193,7 @@ hetionet_analysis/
 ├── neo4j_csv/                  # Generated output directory
 │   ├── nodes_*.csv             # Node files by type (11 files)
 │   ├── edges_all.csv           # All relationships
 |   |── edges_*.csv              # Splitted relationsship for neo4j import
 │   ├── analysis_*.csv          # Analysis results (6 files)
 │   ├── network_nodes.csv       # Network visualization nodes
 │   └── network_edges.csv       # Network visualization edges
@@ -375,16 +404,6 @@ edges_df['target'] = edges_df['target'].astype(str)
 This prevents type mismatch errors when joining dataframes.
 ### Memory Management
 Peak memory usage: ~2GB during ETL processing
 **Optimization strategies**:
 - Process data in chunks where possible
 - Drop intermediate dataframes after use
 - Use generators for large iterations
 ### Edge Direction Conventions
 Hetionet uses directional relationships. Key conventions:
@@ -398,28 +417,6 @@ Hetionet uses directional relationships. Key conventions:
 Current implementation handles Hetionet v1.0 (47K nodes, 2.2M edges).
 For larger datasets:
 - Implement chunked CSV reading
 - Use database backend (PostgreSQL, Neo4j)
 - Parallelize analyses with multiprocessing
 ## Troubleshooting
 ### Common Issues
 **Issue**: "FileNotFoundError: hetionet-v1.0.json"
 **Solution**: Download Hetionet data and place in project directory
 **Issue**: "Module not found"
 **Solution**: Ensure virtual environment is activated and dependencies installed
 **Issue**: Dashboard shows "No data available"
 **Solution**: Run ETL pipeline first to generate CSV files
 **Issue**: "Memory Error" during ETL
 **Solution**: Close other applications or increase system RAM
 ### Data Quality
 The analyses depend on Hetionet data quality. Known limitations:
@@ -428,16 +425,79 @@ The analyses depend on Hetionet data quality. Known limitations:
 - Gene-disease associations vary in evidence strength
 - Network is not exhaustive of all biomedical knowledge
 ## Neo4j ETL & Analysis Pipeline
 This repository includes a script for executing analysis queries on the dataset in a Neo4j database.
 ### Neo4j Prerequisites
 Ensure that the following components are installed and ready to use:
 - **Neo4j Desktop:** A local database instance must be created.
 - **Python 3.x:** Installed on your system.
 - **Python Driver:** Install the official Neo4j driver via pip in your virtual environment you created earlier:
    ```bash
    pip install neo4j
    ```
 ---
 ## Workflow Steps
 Follow these steps exactly in the order provided:
 ### 1. Start Neo4j Database
  Open **Neo4j Desktop**.
  Select your project and click **Start** on the corresponding database.
  The database must be active before proceeding to the next steps.
 ### 2. Copy CSV Files
  After your ETL process has generated the CSV files, they must be moved to the Neo4j import directory.
  **Locating the path:** In Neo4j Desktop, click on `Open Folder` -> `Import`.
  Copy all CSV files from your ETL output into this folder.
 ### 3. Data Import via Cypher
  Navigate to the folder `neo4jqueries/loadingQueriesNeo4j`.
  Execute the Cypher scripts contained there within the **Neo4j Browser**.
  These scripts load the data from the import folder and create the nodes and relationships in the graph.
 ### 4. Execute Python Analysis
  Start the analysis script via your terminal:
 ```bash
 python neo4j_etl.py
 ```
  **Eingabe:** Das Skript wird Sie nacheinander nach Ihrem **Datenbank-Usernamen** (Standard: `neo4j`) und Ihrem **Passwort** fragen.
  **Verarbeitung:** Das Skript liest automatisch alle Abfragen aus dem Verzeichnis `neo4jqueries/analysis_queries` aus.
  **Ausgabe:** Die Ergebnisse der Analyse-Queries werden direkt in der Konsole ausgegeben.
 ---
 ### Projektstruktur
 | Verzeichnis / Datei                 | Funktion                                                   |
 | :---------------------------------- | :--------------------------------------------------------- |
 | `neo4j_etl.py`                      | Das Python-Skript zur Ausführung der Analyse-Queries.      |
 | `neo4jqueries/loadingQueriesNeo4j/` | Enthält alle Cypher-Dateien für den initialen Datenimport. |
 | `neo4jqueries/analysis_queries/`    | Enthält Cypher-Dateien für die statistische Auswertung.    |
 ---
 ## Future Enhancements
 Potential extensions to this project:
-1. **Neo4j Integration**: Direct graph database storage for complex queries
+1. **Machine Learning**: Predictive models for drug efficacy
-2. **Machine Learning**: Predictive models for drug efficacy
+2. **Temporal Analysis**: Track knowledge graph changes over time
-3. **Temporal Analysis**: Track knowledge graph changes over time
+3. **API Development**: REST API for programmatic access
-4. **API Development**: REST API for programmatic access
+4. **Cloud Deployment**: AWS/GCP hosting for web access
-5. **Cloud Deployment**: AWS/GCP hosting for web access
+5. **Additional Data Sources**: Integrate DrugBank, KEGG, etc.
 6. **Additional Data Sources**: Integrate DrugBank, KEGG, etc.
 ## References
--- a/dashboard_update.py
+++ b/dashboard_update.py
@@ -187,7 +187,7 @@ try:
    # HOTSPOT GENES PAGE
    elif page == "Hotspot Genes":
-        st.header("🧬 Hotspot Genes - Most Disease Associations")
+        st.header("Hotspot Genes - Most Disease Associations")
        col1, col2 = st.columns([3, 1])
@@ -347,7 +347,7 @@ try:
        )
        perfect = super_drugs[(super_drugs['num_side_effects'] == 0) & (super_drugs['num_diseases_treated'] > 0)]
-        st.info(f"💎 Found {len(perfect)} drugs with ZERO documented side effects!")
+        st.info(f"Found {len(perfect)} drugs with ZERO documented side effects!")
        csv = filtered_super.to_csv(index=False).encode('utf-8')
        st.download_button("Download Super Drugs", csv, "super_drugs.csv", "text/csv")
--- a/schnellerereETL.py
+++ b/schnellerereETL.py
@@ -2,49 +2,6 @@ import json
 import pandas as pd
 from pathlib import Path
 from collections import defaultdict
 from neo4j import GraphDatabase
 NEO4J_URI = "bolt://localhost:7687"
 NEO4J_USER = "neo4j"
 NEO4J_PASSWORD = "password"
 driver = GraphDatabase.driver(
    NEO4J_URI,
    auth=(NEO4J_USER, NEO4J_PASSWORD)
 )
 def load_nodes(df, label):
    with driver.session() as session:
        for _, row in df.iterrows():
            session.run(
                f"""
                MERGE (n:{label} {{id: $id}})
                SET n += $props
                """,
                id=row["id"],
                props=row.drop("id").dropna().to_dict()
            )
 def load_edges(edges_df):
    with driver.session() as session:
        for _, row in edges_df.iterrows():
            session.run(
                """
                MATCH (s {id: $source})
                MATCH (t {id: $target})
                CALL apoc.create.relationship(s, $type, {}, t)
                YIELD rel
                RETURN rel
                """,
                source=row["source"],
                target=row["target"],
                type=row["type"].upper()
            )
 # KONFIGURATION
@@ -54,7 +11,7 @@ OUTPUT_DIR = Path("neo4j_csv")
 OUTPUT_DIR.mkdir(exist_ok=True)
 print("="*60)
-print("HETIONET ETL PIPELINE")
+print("HETIONET ETL PIPELINE (OPTIMIZED + SPLIT EDGES)")
 print("="*60)
 # EXTRACT
@@ -74,7 +31,7 @@ print(f"Edges loaded: {len(edges_raw):,}")
 # TRANSFORM – NODES
-print("\n PHASE 2: TRANSFORM NODES")
+print("\nPHASE 2: TRANSFORM NODES")
 print("-"*60)
 nodes_flat = []
@@ -113,7 +70,7 @@ symptom_ids = set(nodes_df[nodes_df['kind'] == 'Symptom']['id'])
 compound_ids = set(nodes_df[nodes_df['kind'] == 'Compound']['id'])
 sideeffect_ids = set(nodes_df[nodes_df['kind'] == 'Side Effect']['id'])
-print(f"\n Node Statistics:")
+print(f"\nNode Statistics:")
 print(f"   - Genes: {len(gene_ids):,}")
 print(f"   - Diseases: {len(disease_ids):,}")
 print(f"   - Symptoms: {len(symptom_ids):,}")
@@ -121,7 +78,7 @@ print(f"   - Compounds: {len(compound_ids):,}")
 print(f"   - Side Effects: {len(sideeffect_ids):,}")
 # Export nodes by type
-print("\n Exporting node files...")
+print("\nExporting node files...")
 for kind in nodes_df["kind"].unique():
    df_kind = (
        nodes_df[nodes_df["kind"] == kind]
@@ -153,29 +110,50 @@ edges_df = pd.DataFrame(edges)
 # Relationship-Typen Neo4j-sicher machen
 edges_df["type"] = edges_df["type"].str.replace(" ", "_").str.replace("-", "_")
 # split edges into seperate files
 print("\nExporting edges by type to separate CSV files...")
 print("-"*60)
 edge_types = edges_df['type'].unique()
 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)
    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
 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)")
-print(f"\n Edges processed: {len(edges_df):,}")
+print(f"\nSummary:")
-print(f"Saved to: {edges_file.name}")
+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
+# Pre-filter edges by type for analysis
-print("\n Pre-filtering edges by type...")
+print("\nEdge type distribution:")
 edges_by_type = {}
-for edge_type in ['associates', 'treats', 'presents', 'causes', 'regulates', 'upregulates', 'downregulates', 'binds']:
+for edge_type in sorted(edge_types):
    edges_by_type[edge_type] = edges_df[edges_df['type'] == edge_type].copy()
-    if len(edges_by_type[edge_type]) > 0:
+    count = len(edges_by_type[edge_type])
-        print(f"   - {edge_type}: {len(edges_by_type[edge_type]):,}")
+    pct = 100 * count / len(edges_df)
    print(f"   - {edge_type:20s}: {count:>10,} ({pct:>5.1f}%)")
-
+# [ANALYSES - keeping all the existing analysis code...]
-# ANALYSES
+# (Keeping the same analysis code as before)
 print("\n" + "="*60)
 print("PHASE 4: ANALYSES")
 print("="*60)
 # ANALYSIS 1: HOTSPOT GENES
-print("\n Analysis 1: Hotspot Genes")
+print("\nAnalysis 1: Hotspot Genes")
 print("-"*60)
 gene_disease_edges = pd.concat([
@@ -206,7 +184,7 @@ 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
-print("\n Analysis 2: Disease Symptom Diversity")
+print("\nAnalysis 2: Disease Symptom Diversity")
 print("-"*60)
 disease_symptom_edges = edges_by_type.get('presents', pd.DataFrame())
@@ -231,7 +209,7 @@ 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
-print("\n🔍 Building indices for drug analyses...")
+print("\nBuilding indices for drug analyses...")
 disease_to_genes = defaultdict(set)
 gene_to_diseases = defaultdict(set)
 for _, row in gene_disease_edges.iterrows():
@@ -249,6 +227,17 @@ symptom_to_diseases = defaultdict(set)
 for _, row in disease_symptom_edges.iterrows():
    symptom_to_diseases[row['target']].add(row['source'])
 print("\nETL (Extract, Transform, Load CSV files) COMPLETED!")
 print("="*60)
 print("\n📁 Generated CSV files:")
 print(f"   - Node files: 5")
 print(f"   - Edge files (split): {len(edge_types)}")
 print(f"   - Edge file (combined): 1")
 print(f"   - Analysis files: Various")
 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
 print("\nAnalysis 3: Drug Repurposing Opportunities")
 print("-"*60)
@@ -306,7 +295,7 @@ if len(drug_sideeffects) > 0:
    print(f"Analyzed {len(drug_risk_sorted):,} drugs for side effects")
 # ANALYSIS 5: SYMPTOM TRIANGLE
-print("\n Analysis 5: Symptom-Disease-Drug Triangle")
+print("\nAnalysis 5: Symptom-Disease-Drug Triangle")
 print("-"*60)
 drugs_with_sideeffects_set = set(drug_sideeffects['source']) if len(drug_sideeffects) > 0 else set()
@@ -338,7 +327,7 @@ if len(symptom_triangle_df) > 0:
    print(f"Analyzed {len(symptom_triangle_df):,} symptoms")
 # ANALYSIS 6: SUPER DRUGS
-print("\n Analysis 6: Super-Drug Score")
+print("\nAnalysis 6: Super-Drug Score")
 print("-"*60)
 super_drugs = []
@@ -402,7 +391,7 @@ if len(drug_conflicts_df) > 0:
    print(f"Found {len(drug_conflicts_df):,} drug conflict pairs")
 # ANALYSIS 8: NETWORK DATA
-print("\n🕸️ Analysis 8: Network Visualization Data")
+print("\nAnalysis 8: Network Visualization Data")
 print("-"*60)
 top_diseases = disease_df_sorted.nlargest(20, 'num_symptoms')['id'].tolist()
@@ -476,23 +465,8 @@ network_edges_df = pd.DataFrame(network_edges)
 network_nodes_df.to_csv(OUTPUT_DIR / "network_nodes.csv", index=False)
 network_edges_df.to_csv(OUTPUT_DIR / "network_edges.csv", index=False)
-
+print(f"Created network with {len(network_nodes_df):,} nodes and {len(network_edges_df):,} edges")
 load_nodes(nodes_df[nodes_df['kind']=="Gene"], "Gene")
 load_nodes(nodes_df[nodes_df['kind']=="Disease"], "Disease")
 load_nodes(nodes_df[nodes_df['kind']=="Compound"], "Compound")
 load_nodes(nodes_df[nodes_df['kind']=="Symptom"], "Symptom")
 load_nodes(nodes_df[nodes_df['kind']=="Side Effect"], "SideEffect")
 load_edges(edges_df)
 print(f"Network: {len(network_nodes_df)} nodes, {len(network_edges_df)} edges")
 print("\n" + "="*60)
 print("ETL PIPELINE COMPLETED SUCCESSFULLY")
 print("="*60)
 print(f"\nOutput directory: {OUTPUT_DIR.resolve()}")
--- a/neo4j_csv/analysis_drug_repurposing.csv
+++ b/neo4j_csv/analysis_drug_repurposing.csv
--- a/neo4j_csv/analysis_super_drugs.csv
+++ b/neo4j_csv/analysis_super_drugs.csv
@@ -2,22 +2,22 @@ name,num_diseases_treated,num_side_effects,super_score
 Dacarbazine,7,0,7.0
 Sulfasalazine,4,0,4.0
 Cholecalciferol,3,0,3.0
 Cytarabine,2,0,2.0
 Atorvastatin,2,0,2.0
 Cytarabine,2,0,2.0
 Balsalazide,2,0,2.0
 Carboplatin,9,7,1.125
 Eribulin,1,0,1.0
 Guanethidine,1,0,1.0
 Doxycycline,1,0,1.0
 Isoetarine,1,0,1.0
 Moexipril,1,0,1.0
 Olsalazine,1,0,1.0
 Rifampicin,1,0,1.0
 Vitamin C,1,0,1.0
 Eribulin,1,0,1.0
 Artemether,1,0,1.0
-Guanethidine,1,0,1.0
+Moexipril,1,0,1.0
 Lopinavir,1,0,1.0
-Guanadrel,1,0,1.0
+Rifampicin,1,0,1.0
 Minoxidil,1,0,1.0
 Guanadrel,1,0,1.0
 Vitamin C,1,0,1.0
 Physostigmine,1,6,0.14285714285714285
 Dactinomycin,10,75,0.13157894736842105
 Vinblastine,7,59,0.11666666666666667
@@ -39,17 +39,17 @@ Methoxsalen,3,53,0.05555555555555555
 Mercaptopurine,3,57,0.05172413793103448
 Penbutolol,2,39,0.05
 Vincristine,7,142,0.04895104895104895
 Chlorambucil,4,83,0.047619047619047616
 Tiludronate,1,20,0.047619047619047616
 Chlorambucil,4,83,0.047619047619047616
 Cisplatin,8,169,0.047058823529411764
 Methylprednisolone,10,216,0.04608294930875576
 Calcipotriol,1,21,0.045454545454545456
 Beclomethasone,2,43,0.045454545454545456
 Methamphetamine,1,21,0.045454545454545456
 Calcipotriol,1,21,0.045454545454545456
 Fluocinonide,2,44,0.044444444444444446
 Lomustine,2,44,0.044444444444444446
 Dexamethasone,11,249,0.044
 Betamethasone,11,249,0.044
 Dexamethasone,11,249,0.044
 Teniposide,3,70,0.04225352112676056
 Miglitol,1,23,0.041666666666666664
 Benzphetamine,1,23,0.041666666666666664
@@ -58,16 +58,16 @@ Loratadine,3,73,0.04054054054054054
 Mechlorethamine,2,49,0.04
 Auranofin,2,50,0.0392156862745098
 Altretamine,1,25,0.038461538461538464
 Topotecan,4,111,0.03571428571428571
 Dyphylline,1,27,0.03571428571428571
 Topotecan,4,111,0.03571428571428571
 Mecamylamine,1,27,0.03571428571428571
 Spironolactone,2,56,0.03508771929824561
 Proguanil,1,28,0.034482758620689655
 Triamterene,1,28,0.034482758620689655
 Probenecid,1,28,0.034482758620689655
 Hydrocortisone,8,231,0.034482758620689655
 Diphenhydramine,2,57,0.034482758620689655
 Dimenhydrinate,2,57,0.034482758620689655
 Triamterene,1,28,0.034482758620689655
 Probenecid,1,28,0.034482758620689655
 Proguanil,1,28,0.034482758620689655
 Melphalan,4,117,0.03389830508474576
 Irinotecan,6,177,0.033707865168539325
 Mitoxantrone,6,177,0.033707865168539325
@@ -88,43 +88,43 @@ Bendroflumethiazide,1,34,0.02857142857142857
 Carmustine,5,177,0.028089887640449437
 Mometasone,3,107,0.027777777777777776
 Epirubicin,14,511,0.02734375
 Valsartan,3,110,0.02702702702702703
 Methimazole,1,36,0.02702702702702703
 Valsartan,3,110,0.02702702702702703
 Acetazolamide,2,74,0.02666666666666667
 Tolazamide,1,38,0.02564102564102564
 Chlorpropamide,1,38,0.02564102564102564
 Metipranolol,1,38,0.02564102564102564
 Chlorpropamide,1,38,0.02564102564102564
 Linagliptin,1,39,0.025
 Disulfiram,1,39,0.025
 Propantheline,1,39,0.025
 Disulfiram,1,39,0.025
 Rosuvastatin,2,80,0.024691358024691357
 Hydroflumethiazide,1,40,0.024390243902439025
 Vinorelbine,4,164,0.024242424242424242
 Amobarbital,1,41,0.023809523809523808
 Acarbose,1,42,0.023255813953488372
 Acetylsalicylic acid,2,86,0.022988505747126436
 Reserpine,1,43,0.022727272727272728
 Phenylpropanolamine,1,43,0.022727272727272728
 Ethacrynic acid,1,43,0.022727272727272728
 Reserpine,1,43,0.022727272727272728
 Montelukast,3,132,0.022556390977443608
 Colchicine,2,88,0.02247191011235955
 Calcium Acetate,1,44,0.022222222222222223
 Ruxolitinib,1,44,0.022222222222222223
 Vemurafenib,2,90,0.02197802197802198
 Primidone,1,45,0.021739130434782608
 Tazarotene,1,45,0.021739130434782608
 Primidone,1,45,0.021739130434782608
 Thiotepa,4,184,0.021621621621621623
 Metformin,3,139,0.02142857142857143
 Carteolol,1,46,0.02127659574468085
 Nateglinide,1,46,0.02127659574468085
 Carteolol,1,46,0.02127659574468085
 Alendronate,2,94,0.021052631578947368
 Aminophylline,2,95,0.020833333333333332
 Fenoldopam,1,47,0.020833333333333332
-Ketotifen,1,48,0.02040816326530612
+Aminophylline,2,95,0.020833333333333332
 Diclofenamide,1,48,0.02040816326530612
 Ketotifen,1,48,0.02040816326530612
 Phentermine,1,48,0.02040816326530612
 Raloxifene,2,98,0.020202020202020204
 Idarubicin,2,98,0.020202020202020204
 Raloxifene,2,98,0.020202020202020204
 Captopril,3,148,0.020134228187919462
 Pitavastatin,1,49,0.02
 Erlotinib,3,152,0.0196078431372549
@@ -132,39 +132,39 @@ Nebivolol,1,50,0.0196078431372549
 Glyburide,2,102,0.019417475728155338
 Ramipril,4,206,0.01932367149758454
 Carbachol,1,51,0.019230769230769232
 Desonide,1,52,0.018867924528301886
 Valrubicin,1,52,0.018867924528301886
 Desonide,1,52,0.018867924528301886
 Zileuton,1,52,0.018867924528301886
 Calcitriol,2,106,0.018691588785046728
 Levobunolol,1,53,0.018518518518518517
 Timolol,4,218,0.0182648401826484
 Propylthiouracil,1,54,0.01818181818181818
 Vismodegib,1,54,0.01818181818181818
 Propylthiouracil,1,54,0.01818181818181818
 Indacaterol,1,54,0.01818181818181818
 Tiotropium,2,110,0.018018018018018018
 Orciprenaline,1,55,0.017857142857142856
 Pentoxifylline,2,111,0.017857142857142856
 Propranolol,2,112,0.017699115044247787
 Furosemide,3,171,0.01744186046511628
 Torasemide,1,58,0.01694915254237288
 Nedocromil,1,58,0.01694915254237288
-Pirbuterol,1,59,0.016666666666666666
+Torasemide,1,58,0.01694915254237288
 Methazolamide,1,59,0.016666666666666666
 Pirbuterol,1,59,0.016666666666666666
 Temozolomide,4,241,0.01652892561983471
 Lovastatin,2,121,0.01639344262295082
 Losartan,3,187,0.015957446808510637
 Trimethadione,1,62,0.015873015873015872
 Chlorothiazide,1,62,0.015873015873015872
 Trimethadione,1,62,0.015873015873015872
 Ezetimibe,2,127,0.015625
 Entecavir,1,63,0.015625
 Latanoprost,1,64,0.015384615384615385
 Chlorthalidone,1,65,0.015151515151515152
 Fluticasone furoate,1,65,0.015151515151515152
 Lisinopril,4,263,0.015151515151515152
 Fluticasone furoate,1,65,0.015151515151515152
 Sorafenib,3,198,0.01507537688442211
 Bumetanide,1,66,0.014925373134328358
 Estramustine,1,66,0.014925373134328358
 Repaglinide,1,66,0.014925373134328358
 Estramustine,1,66,0.014925373134328358
 Prazosin,1,66,0.014925373134328358
 Flunisolide,2,133,0.014925373134328358
 Eplerenone,2,134,0.014814814814814815
@@ -172,25 +172,25 @@ Vorinostat,1,67,0.014705882352941176
 Adefovir Dipivoxil,1,67,0.014705882352941176
 Cyproheptadine,1,68,0.014492753623188406
 Procarbazine,2,137,0.014492753623188406
 Clobetasol propionate,1,68,0.014492753623188406
 Diethylpropion,1,68,0.014492753623188406
 Clobetasol propionate,1,68,0.014492753623188406
 Verapamil,2,138,0.014388489208633094
 Roflumilast,1,69,0.014285714285714285
 Dextrothyroxine,1,71,0.013888888888888888
 Abiraterone,1,71,0.013888888888888888
 Levothyroxine,1,71,0.013888888888888888
-Simvastatin,2,143,0.013888888888888888
+Abiraterone,1,71,0.013888888888888888
 Fluocinolone Acetonide,2,143,0.013888888888888888
-Fludarabine,2,145,0.0136986301369863
+Simvastatin,2,143,0.013888888888888888
 Dextrothyroxine,1,71,0.013888888888888888
 Floxuridine,1,72,0.0136986301369863
 Fludarabine,2,145,0.0136986301369863
 Lapatinib,1,73,0.013513513513513514
 Amprenavir,1,73,0.013513513513513514
 Desloratadine,1,73,0.013513513513513514
 Estrone,1,74,0.013333333333333334
 Rosiglitazone,1,76,0.012987012987012988
 Bepridil,1,76,0.012987012987012988
 Zafirlukast,1,76,0.012987012987012988
 Ethinyl Estradiol,2,153,0.012987012987012988
 Rosiglitazone,1,76,0.012987012987012988
 Zafirlukast,1,76,0.012987012987012988
 Bepridil,1,76,0.012987012987012988
 Cimetidine,1,77,0.01282051282051282
 Sulfadiazine,1,78,0.012658227848101266
 Leflunomide,3,240,0.012448132780082987
@@ -201,21 +201,21 @@ Candesartan,1,83,0.011904761904761904
 Phenobarbital,1,84,0.011764705882352941
 Orlistat,2,170,0.011695906432748537
 Cyclosporine,4,344,0.011594202898550725
 Theophylline,1,86,0.011494252873563218
 Methyldopa,1,86,0.011494252873563218
 Theophylline,1,86,0.011494252873563218
 Niacin,2,177,0.011235955056179775
 Rufinamide,1,88,0.011235955056179775
 Nilutamide,1,89,0.011111111111111112
 Fingolimod,1,90,0.01098901098901099
 Fulvestrant,1,90,0.01098901098901099
 Amiloride,1,90,0.01098901098901099
-Flutamide,1,90,0.01098901098901099
+Fulvestrant,1,90,0.01098901098901099
 Fingolimod,1,90,0.01098901098901099
 Hydralazine,1,90,0.01098901098901099
 Flutamide,1,90,0.01098901098901099
 Pravastatin,2,184,0.010810810810810811
 Didanosine,1,92,0.010752688172043012
 Pamidronate,2,185,0.010752688172043012
-Clonidine,2,186,0.0106951871657754
+Didanosine,1,92,0.010752688172043012
 Cladribine,2,186,0.0106951871657754
 Clonidine,2,186,0.0106951871657754
 Toremifene,1,93,0.010638297872340425
 Pindolol,1,93,0.010638297872340425
 Formoterol,2,188,0.010582010582010581
@@ -227,18 +227,18 @@ Crizotinib,1,99,0.01
 Enalapril,2,200,0.009950248756218905
 Benazepril,1,101,0.00980392156862745
 Nadolol,1,102,0.009708737864077669
 Ifosfamide,3,311,0.009615384615384616
 Telbivudine,1,103,0.009615384615384616
 Lacosamide,1,103,0.009615384615384616
 Ifosfamide,3,311,0.009615384615384616
 Telmisartan,2,207,0.009615384615384616
 Telbivudine,1,103,0.009615384615384616
 Estradiol valerate/Dienogest,1,104,0.009523809523809525
 Isradipine,1,106,0.009345794392523364
 Glipizide,1,106,0.009345794392523364
 Isradipine,1,106,0.009345794392523364
 Guanfacine,1,107,0.009259259259259259
 Lamivudine,2,216,0.009216589861751152
 Salbutamol,2,217,0.009174311926605505
 Olopatadine,1,108,0.009174311926605505
 Nicardipine,1,108,0.009174311926605505
 Salbutamol,2,217,0.009174311926605505
 Paclitaxel,4,442,0.009029345372460496
 Tamoxifen,2,222,0.008968609865470852
 Felodipine,1,111,0.008928571428571428
@@ -248,8 +248,8 @@ Apraclonidine,1,112,0.008849557522123894
 Zoledronate,2,232,0.008583690987124463
 Gemfibrozil,1,116,0.008547008547008548
 Budesonide,2,235,0.00847457627118644
 Pioglitazone,1,118,0.008403361344537815
 Degarelix,1,118,0.008403361344537815
 Pioglitazone,1,118,0.008403361344537815
 Esmolol,1,118,0.008403361344537815
 Perindopril,2,239,0.008333333333333333
 Nevirapine,1,120,0.008264462809917356
@@ -261,15 +261,15 @@ Penicillamine,1,122,0.008130081300813009
 Hydrochlorothiazide,2,246,0.008097165991902834
 Ticagrelor,1,123,0.008064516129032258
 Pimecrolimus,1,124,0.008
 Goserelin,2,249,0.008
 Pemetrexed,1,124,0.008
-Glimepiride,1,126,0.007874015748031496
+Goserelin,2,249,0.008
 Gefitinib,1,126,0.007874015748031496
-Conjugated Estrogens,2,255,0.0078125
+Glimepiride,1,126,0.007874015748031496
 Sitagliptin,1,127,0.0078125
 Conjugated Estrogens,2,255,0.0078125
 Exemestane,1,128,0.007751937984496124
 Nelfinavir,1,130,0.007633587786259542
 Acebutolol,1,130,0.007633587786259542
 Nelfinavir,1,130,0.007633587786259542
 Stavudine,1,131,0.007575757575757576
 Betaxolol,2,263,0.007575757575757576
 Labetalol,1,133,0.007462686567164179
@@ -285,30 +285,30 @@ Nisoldipine,1,155,0.00641025641025641
 Amlodipine,1,155,0.00641025641025641
 Mycophenolate mofetil,3,488,0.006134969325153374
 Valproic Acid,2,326,0.0061162079510703364
 Clindamycin,1,163,0.006097560975609756
 Sunitinib,2,327,0.006097560975609756
 Bromocriptine,1,163,0.006097560975609756
 Clindamycin,1,163,0.006097560975609756
 Brimonidine,1,164,0.006060606060606061
 Fluticasone Propionate,1,166,0.005988023952095809
 Salmeterol,1,167,0.005952380952380952
 Quinidine,1,171,0.005813953488372093
 Quinine,1,171,0.005813953488372093
 Quinidine,1,171,0.005813953488372093
 Anastrozole,1,172,0.005780346820809248
 Letrozole,1,172,0.005780346820809248
 Daunorubicin,1,177,0.0056179775280898875
 Clofarabine,1,177,0.0056179775280898875
 Topiramate,3,535,0.005597014925373134
 Propofol,1,178,0.00558659217877095
 Pentostatin,1,179,0.005555555555555556
 Anagrelide,1,179,0.005555555555555556
-Estropipate,1,181,0.005494505494505495
+Pentostatin,1,179,0.005555555555555556
 Metoprolol,1,181,0.005494505494505495
 Guanabenz,1,181,0.005494505494505495
 Metoprolol,1,181,0.005494505494505495
 Estropipate,1,181,0.005494505494505495
 Galantamine,1,184,0.005405405405405406
 Dorzolamide,1,184,0.005405405405405406
 Nicotine,1,185,0.005376344086021506
 Travoprost,1,186,0.0053475935828877
 Zidovudine,1,186,0.0053475935828877
 Travoprost,1,186,0.0053475935828877
 Midazolam,1,188,0.005291005291005291
 Brinzolamide,1,192,0.0051813471502590676
 Levetiracetam,1,194,0.005128205128205128
@@ -323,15 +323,15 @@ Hyoscyamine,1,208,0.004784688995215311
 Pilocarpine,1,212,0.004694835680751174
 Fosphenytoin,1,212,0.004694835680751174
 Quinidine barbiturate,1,221,0.0045045045045045045
 Vandetanib,1,222,0.004484304932735426
 Estradiol,2,445,0.004484304932735426
 Vandetanib,1,222,0.004484304932735426
 Indinavir,1,223,0.004464285714285714
 Vigabatrin,1,226,0.004405286343612335
 Alitretinoin,2,453,0.004405286343612335
 Vigabatrin,1,226,0.004405286343612335
 Fenofibrate,1,228,0.004366812227074236
 Acamprosate,1,229,0.004347826086956522
 Chenodeoxycholic acid,1,230,0.004329004329004329
 Ursodeoxycholic acid,1,230,0.004329004329004329
 Chenodeoxycholic acid,1,230,0.004329004329004329
 Pazopanib,1,231,0.004310344827586207
 Diazepam,1,235,0.00423728813559322
 Clomifene,1,236,0.004219409282700422
@@ -340,8 +340,8 @@ Indapamide,1,243,0.004098360655737705
 Diltiazem,1,243,0.004098360655737705
 Carvedilol,1,249,0.004
 Busulfan,1,254,0.00392156862745098
 Allopurinol,1,255,0.00390625
 Felbamate,1,255,0.00390625
 Allopurinol,1,255,0.00390625
 Cetirizine,1,255,0.00390625
 Doxazosin,1,256,0.0038910505836575876
 Efavirenz,1,257,0.003875968992248062
@@ -356,12 +356,12 @@ Acitretin,1,284,0.0035087719298245615
 Delavirdine,1,287,0.003472222222222222
 Bexarotene,1,288,0.0034602076124567475
 Dasatinib,1,288,0.0034602076124567475
 Clonazepam,1,295,0.0033783783783783786
 Octreotide,1,295,0.0033783783783783786
 Clonazepam,1,295,0.0033783783783783786
 Varenicline,1,297,0.003355704697986577
 Phenytoin,1,300,0.0033222591362126247
 Esomeprazole,1,302,0.0033003300330033004
 Omeprazole,1,302,0.0033003300330033004
 Esomeprazole,1,302,0.0033003300330033004
 Oxcarbazepine,1,304,0.003278688524590164
 Progesterone,1,315,0.0031645569620253164
 Carbamazepine,1,337,0.0029585798816568047
@@ -377,8 +377,8 @@ Medroxyprogesterone Acetate,1,403,0.0024752475247524753
 Riluzole,1,408,0.0024449877750611247
 Everolimus,1,417,0.0023923444976076554
 Memantine,1,443,0.0022522522522522522
 Isotretinoin,1,453,0.0022026431718061676
 Tretinoin,1,453,0.0022026431718061676
 Isotretinoin,1,453,0.0022026431718061676
 Rivastigmine,1,468,0.0021321961620469083
 Lenalidomide,1,502,0.0019880715705765406
 Bupropion,1,520,0.0019193857965451055
--- a/neo4j_csv/analysis_symptom_triangle.csv
+++ b/neo4j_csv/analysis_symptom_triangle.csv
@@ -1,95 +1,95 @@
 symptom,num_diseases,num_treating_drugs,drugs_with_side_effects,impact_score
-Vomiting,38,123,114,4674
+Edema,49,216,204,10584
-Fatigue,25,137,131,3425
+Body Weight,23,169,159,3887
-Psychophysiologic Disorders,18,170,160,3060
+Fever of Unknown Origin,25,107,98,2675
-Birth Weight,14,168,159,2352
+Neurologic Manifestations,28,84,79,2352
 Paresthesia,26,87,83,2262
 "Hearing Loss, Sensorineural",24,70,65,1680
-Dyspnea,21,77,75,1617
+Constipation,21,75,70,1575
-Cerebellar Ataxia,17,84,82,1428
+Polyuria,12,101,96,1212
-Cough,16,86,83,1376
+Facial Pain,17,66,63,1122
-"Purpura, Thrombocytopenic, Idiopathic",15,89,83,1335
+Sensation Disorders,16,68,64,1088
-Muscular Atrophy,16,81,77,1296
+Facial Paralysis,19,56,53,1064
-Sleep Apnea Syndromes,12,95,90,1140
+Quadriplegia,17,61,59,1037
-Ataxia,16,68,67,1088
+Oral Manifestations,15,62,57,930
-Intellectual Disability,17,59,58,1003
+Pelvic Pain,17,49,47,833
-"Purpura, Thrombocytopenic",14,70,64,980
+Hearing Disorders,18,46,45,828
-Flushing,12,75,73,900
+Hemoptysis,16,50,49,800
-Asthenia,13,68,66,884
+Angina Pectoris,8,85,80,680
-"Obesity, Abdominal",8,108,103,864
+Scotoma,12,55,54,660
-Sciatica,15,50,48,750
+Weight Loss,9,62,60,558
-Aphasia,13,57,57,741
+Horner Syndrome,13,42,41,546
-Respiratory Sounds,11,67,66,737
+Hearing Loss,15,36,35,540
-Ataxia Telangiectasia,13,49,47,637
+Torticollis,14,37,35,518
 Vertigo,13,48,46,624
 "Urinary Bladder, Neurogenic",16,37,36,592
 "Purpura, Thrombotic Thrombocytopenic",7,84,81,588
 Syncope,11,48,47,528
 Hyperventilation,7,74,73,518
 Cyanosis,8,64,63,512
 Amnesia,10,50,50,500
 Hyperalgesia,10,48,46,480
 Voice Disorders,9,52,51,468
 Emaciation,8,57,54,456
-Flatulence,8,53,48,424
+Dysphonia,8,55,53,440
-Cerebrospinal Fluid Rhinorrhea,10,39,38,390
+"Jaundice, Obstructive",14,30,29,420
-Eye Hemorrhage,8,47,44,376
+Mobility Limitation,9,44,42,396
-"Hearing Loss, High-Frequency",10,37,36,370
+Pupil Disorders,10,37,37,370
 Chronic Pain,8,46,44,368
-Olfaction Disorders,10,36,36,360
+"Purpura, Schoenlein-Henoch",10,36,32,360
-"Urinary Bladder, Overactive",6,59,58,354
+Dystonia,9,37,37,333
-Color Vision Defects,9,39,39,351
+"Gait Disorders, Neurologic",11,26,25,286
-Acute Coronary Syndrome,6,47,46,282
+Tonic Pupil,5,57,56,285
-Pruritus Ani,6,43,39,258
+Vitreous Hemorrhage,8,34,33,272
-Eye Pain,7,36,35,252
+Agraphia,8,32,32,256
 Agnosia,7,36,36,252
-"Hearing Loss, Bilateral",8,30,29,240
+Eye Pain,7,36,35,252
-"Hearing Loss, Noise-Induced",3,80,74,240
+Perceptual Disorders,11,22,22,242
 "Diarrhea, Infantile",7,34,28,238
 Tremor,5,46,45,230
-Choroid Hemorrhage,6,34,33,204
+"Aging, Premature",6,38,36,228
-Sarcopenia,5,39,38,195
+"Sleep Apnea, Central",5,45,45,225
-"Hearing Loss, Central",6,32,31,192
+Tinea Pedis,5,43,41,215
-Hiccup,7,27,27,189
+Pain Threshold,7,29,28,203
 "Dyslexia, Acquired",7,27,27,189
 "Aphasia, Wernicke",5,36,36,180
 Phantom Limb,6,30,28,180
-Thinness,5,36,35,180
+Hyphema,5,34,33,170
 Cerebrospinal Fluid Leak,7,24,23,168
 Prosopagnosia,5,33,33,165
 Postoperative Nausea and Vomiting,4,39,38,156
 Bulimia,9,17,17,153
 Stuttering,6,25,25,150
-Gait Ataxia,6,22,22,132
+Kearns-Sayre Syndrome,5,30,30,150
 Hydrops Fetalis,6,22,22,132
 Huntington Disease,8,16,16,128
-"Syncope, Vasovagal",4,32,32,128
+Catalepsy,5,25,25,125
-Miosis,4,27,27,108
+Chills,3,39,35,117
-Athetosis,3,36,36,108
+Pseudobulbar Palsy,3,37,37,111
-"Purpura, Hyperglobulinemic",4,24,20,96
+Photophobia,4,27,27,108
-Metatarsalgia,3,27,25,81
+Seizures,3,32,32,96
-Toothache,5,16,16,80
+Pruritus Vulvae,3,30,28,90
-Ageusia,3,26,24,78
+Sneezing,2,43,42,86
-Rett Syndrome,3,25,25,75
+"Angina, Unstable",3,28,27,84
 Parasomnias,3,25,25,75
 De Lange Syndrome,4,18,14,72
 Post-Exercise Hypotension,1,68,63,68
-Fasciculation,4,15,15,60
+"Paraparesis, Spastic",4,16,16,64
-Dyssomnias,2,29,28,58
+"Amnesia, Transient Global",2,29,29,58
 Hyperemesis Gravidarum,3,18,18,54
 Respiratory Paralysis,4,13,13,52
-Nocturnal Paroxysmal Dystonia,2,25,25,50
+Muscle Spasticity,4,12,12,48
-Hyperesthesia,3,14,13,42
+Hyperoxia,3,16,16,48
 Hyperphagia,3,15,15,45
 Anomia,4,11,11,44
 Gerstmann Syndrome,3,13,13,39
-Polydipsia,4,9,9,36
+Breakthrough Pain,2,18,18,36
-Urinoma,3,12,12,36
+Neuroacanthocytosis,1,25,25,25
 Eructation,2,15,13,30
 Ophthalmoplegic Migraine,2,14,14,28
 Sleep Arousal Disorders,1,25,25,25
 Aphonia,3,8,8,24
-Reticulocytosis,2,11,9,22
+Presbycusis,4,6,6,24
 Adrenoleukodystrophy,2,11,11,22
-Muscle Hypertonia,2,11,11,22
+WAGR Syndrome,1,13,13,13
 Halitosis,4,4,4,16
 "Apraxia, Ideomotor",3,5,5,15
 Orthostatic Intolerance,1,11,11,11
 Prader-Willi Syndrome,1,11,11,11
 Susac Syndrome,1,11,11,11
 "Aphasia, Primary Progressive",2,4,4,8
-Benign Paroxysmal Positional Vertigo,1,7,7,7
+Alice in Wonderland Syndrome,1,7,7,7
-Anhedonia,4,1,1,4
+Usher Syndromes,2,2,2,4
-Striae Distensae,1,2,2,2
+Waterhouse-Friderichsen Syndrome,1,0,0,0
-Fetal Weight,2,1,1,2
+"Polydipsia, Psychogenic",1,0,0,0
-Wolfram Syndrome,1,1,1,1
+Gait Apraxia,1,0,0,0
-"Dyskinesia, Drug-Induced",5,0,0,0
+Machado-Joseph Disease,2,0,0,0
-Nocturnal Myoclonus Syndrome,5,0,0,0
+Alien Hand Syndrome,1,0,0,0
 Lipoid Proteinosis of Urbach and Wiethe,1,0,0,0
 "Akathisia, Drug-Induced",7,0,0,0
 Systolic Murmurs,1,0,0,0
--- a/neo4j_csv/edges_associates.csv
+++ b/neo4j_csv/edges_associates.csv
--- a/neo4j_csv/edges_binds.csv
+++ b/neo4j_csv/edges_binds.csv
--- a/neo4j_csv/edges_causes.csv
+++ b/neo4j_csv/edges_causes.csv
--- a/neo4j_csv/edges_covaries.csv
+++ b/neo4j_csv/edges_covaries.csv
--- a/neo4j_csv/edges_downregulates.csv
+++ b/neo4j_csv/edges_downregulates.csv
--- a/neo4j_csv/edges_expresses.csv
+++ b/neo4j_csv/edges_expresses.csv
--- a/neo4j_csv/edges_includes.csv
+++ b/neo4j_csv/edges_includes.csv
--- a/neo4j_csv/edges_interacts.csv
+++ b/neo4j_csv/edges_interacts.csv
--- a/neo4j_csv/edges_localizes.csv
+++ b/neo4j_csv/edges_localizes.csv
--- a/neo4j_csv/edges_palliates.csv
+++ b/neo4j_csv/edges_palliates.csv
@@ -0,0 +1,391 @@
 source,target
 DB01175,DOID:3312
 DB00321,DOID:7148
 DB00176,DOID:594
 DB01037,DOID:10652
 DB00945,DOID:9074
 DB01100,DOID:11119
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 DB01224,DOID:10652
 DB00482,DOID:7148
 DB00564,DOID:2377
 DB00500,DOID:7147
 DB00623,DOID:5419
 DB00820,DOID:418
 DB00668,DOID:4481
 DB00258,DOID:784
 DB00413,DOID:0050425
 DB00424,DOID:14330
 DB00424,DOID:8778
 DB00203,DOID:9744
 DB00343,DOID:3393
 DB00186,DOID:594
 DB00285,DOID:1094
 DB00540,DOID:2377
 DB01233,DOID:6364
 DB00706,DOID:10283
 DB00304,DOID:11612
 DB00230,DOID:2377
 DB01068,DOID:12849
 DB00814,DOID:8398
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 DB00321,DOID:2377
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 DB00502,DOID:11119
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 DB01401,DOID:7148
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 DB06637,DOID:2377
 DB00494,DOID:14330
 DB01234,DOID:1319
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 DB01577,DOID:1094
 DB00502,DOID:12995
 DB01364,DOID:2841
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 DB01589,DOID:2377
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 DB01238,DOID:12849
 DB00477,DOID:5419
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 DB01247,DOID:594
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 DB00181,DOID:2377
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 DB01234,DOID:263
 DB00555,DOID:5419
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 DB00564,DOID:3312
 DB00215,DOID:594
 DB00338,DOID:10283
 DB00715,DOID:1595
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 DB00246,DOID:5419
 DB00788,DOID:7147
 DB01036,DOID:2377
 DB00216,DOID:6364
 DB00953,DOID:6364
 DB00328,DOID:6364
 DB01234,DOID:10283
 DB00655,DOID:1612
 DB01104,DOID:594
 DB01623,DOID:5419
 DB00285,DOID:594
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 DB00323,DOID:14330
 DB00443,DOID:263
 DB00268,DOID:14330
 DB01198,DOID:2377
 DB01238,DOID:1595
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 DB00434,DOID:5419
 DB00687,DOID:14330
 DB01115,DOID:3393
 DB06151,DOID:3083
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 DB00959,DOID:8398
 DB00434,DOID:6364
 DB00575,DOID:12849
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 DB00482,DOID:418
 DB00996,DOID:0050425
 DB00203,DOID:2377
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 DB00745,DOID:14330
 DB00991,DOID:8398
 DB01238,DOID:5419
 DB00572,DOID:14330
 DB00482,DOID:7147
 DB00104,DOID:1793
 DB00502,DOID:5419
 DB00745,DOID:2377
 DB00933,DOID:5419
 DB00122,DOID:8398
 DB00246,DOID:11119
 DB01591,DOID:2377
 DB00998,DOID:6364
 DB00554,DOID:7147
 DB01224,DOID:5419
 DB00586,DOID:8398
 DB00783,DOID:1612
 DB00313,DOID:3312
 DB00564,DOID:5419
 DB00887,DOID:784
 DB01576,DOID:1094
 DB01068,DOID:3312
--- a/neo4j_csv/edges_participates.csv
+++ b/neo4j_csv/edges_participates.csv
--- a/neo4j_csv/edges_presents.csv
+++ b/neo4j_csv/edges_presents.csv
--- a/neo4j_csv/edges_regulates.csv
+++ b/neo4j_csv/edges_regulates.csv
--- a/neo4j_csv/edges_resembles.csv
+++ b/neo4j_csv/edges_resembles.csv
--- a/neo4j_csv/edges_treats.csv
+++ b/neo4j_csv/edges_treats.csv
@@ -0,0 +1,756 @@
 source,target
 DB00997,DOID:363
 DB00206,DOID:10763
 DB00960,DOID:10763
 DB00665,DOID:10283
 DB00290,DOID:2998
 DB01232,DOID:635
 DB00555,DOID:1826
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 DB00635,DOID:2531
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 DB01244,DOID:10763
 DB00888,DOID:2531
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 DB00401,DOID:10763
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 DB01280,DOID:2531
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 DB00250,DOID:12365
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 DB01047,DOID:3310
 DB00635,DOID:2841
 DB00541,DOID:2531
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 DB01234,DOID:9008
 DB00688,DOID:418
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 DB00570,DOID:2998
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--- a/neo4j_csv/edges_upregulates.csv
+++ b/neo4j_csv/edges_upregulates.csv
--- a/neo4j_etl.py
+++ b/neo4j_etl.py
@@ -0,0 +1,81 @@
 from neo4j import GraphDatabase
 import pandas as pd
 import os
 import glob
 import getpass
 # Neo4j Connection
 NEO4J_URI = "bolt://localhost:7687"
 NEO4J_USER = input("Neo4j username: ")
 NEO4J_PASSWORD = getpass.getpass("Neo4j password: ")
 driver = GraphDatabase.driver(
    NEO4J_URI,
    auth=(NEO4J_USER, NEO4J_PASSWORD)
 )
 # Helper Functions
 def test_connection():
    try:    
        with driver.session() as session:
            result = session.run("RETURN 1")
            if result.single():
                print("✓ Connection successful")
                return True
            else:
                print("✗ Error connecting")
                return False
    except Exception as e:
        print(f"✗ Error with the connection: {e}")
        return False
 def run_query(query, parameters=None):
    """Run a Cypher query and return a Pandas DataFrame"""
    with driver.session() as session:
        result = session.run(query, parameters)
        df = pd.DataFrame([record.data() for record in result])
        return df
 # Check Neo4j connection
 if not test_connection():
    print("Cannot connect to Neo4j")
    exit(1)
 # Folder for results
 output_dir = "query_results"
 os.makedirs(output_dir, exist_ok=True)
 # Run all .cypher files in 'queries/' folder
 cypher_files = sorted(glob.glob("analysis_queries/*.cypher"))
 for file in cypher_files:
    with open(file, "r", encoding="utf-8") as f:
        query = f.read()
    print(f"\nRunning {file}")
    try:
        df = run_query(query)
        if df.empty:
            print("⚠ No results returned")
        else:
            print(df.head(5))  # show top 5 rows
            safe_name = os.path.splitext(os.path.basename(file))[0]
            csv_path = os.path.join(output_dir, f"{safe_name}.csv")
            df.to_csv(csv_path, index=False, encoding="utf-8-sig")
            print(f"✓ Saved to {csv_path}")
    except Exception as e:
        print(f"✗ Error running query '{file}': {e}")
 driver.close()
 print("\nAll queries executed.")
--- a/neo4jqueries/analysis_queries/query1.cypher
+++ b/neo4jqueries/analysis_queries/query1.cypher
@@ -0,0 +1,6 @@
 // Drug similarity based on shared targets
 MATCH (c1:Compound)-[:BINDS]->(g:Gene)<-[:BINDS]-(c2:Compound)
 WHERE c1 <> c2
 RETURN c1.name AS Drug1, c2.name AS Drug2, count(g) AS SharedGenes
 ORDER BY SharedGenes DESC
 LIMIT 20;
--- a/neo4jqueries/analysis_queries/query2.cypher
+++ b/neo4jqueries/analysis_queries/query2.cypher
@@ -0,0 +1,4 @@
 // Get count of nodes
 MATCH (n)
 RETURN labels(n) AS NodeType, count(*) AS Count
 ORDER BY Count DESC;
--- a/neo4jqueries/analysis_queries/query3.cypher
+++ b/neo4jqueries/analysis_queries/query3.cypher
@@ -0,0 +1,5 @@
 // Drugs treating diseases that present specific symptoms
 MATCH (s:Symptom)<-[:PRESENTS]-(d:Disease)<-[:TREATS]-(c:Compound)
 RETURN s.name AS Symptom, c.name AS Drug, count(d) AS DiseaseCount
 ORDER BY DiseaseCount DESC
 LIMIT 20;
--- a/neo4jqueries/analysis_queries/query4.cypher
+++ b/neo4jqueries/analysis_queries/query4.cypher
@@ -0,0 +1,5 @@
 // Top 10 diseases with the most associated symptoms
 MATCH (d:Disease)-[:PRESENTS]->(s:Symptom)
 RETURN d.name AS Disease, count(s) AS SymptomCount
 ORDER BY SymptomCount DESC
 LIMIT 10;
--- a/neo4jqueries/analysis_queries/query5.cypher
+++ b/neo4jqueries/analysis_queries/query5.cypher
@@ -0,0 +1,9 @@
 // Drugs sharing the same side effects showing potential conflicts
 MATCH (c1:Compound)-[:BINDS]->(:Gene),
      (c1)-[:TREATS]->(:Disease),
      (c1)-[:TREATS]->(:Disease)<-[:TREATS]-(c2:Compound),
      (c1)-[:BINDS]->(g:Gene)
 WHERE c1 <> c2
 RETURN c1.name AS Drug1, c2.name AS Drug2, count(g) AS SharedTargets
 ORDER BY SharedTargets DESC
 LIMIT 20;
--- a/neo4jqueries/analysis_queries/query6.cypher
+++ b/neo4jqueries/analysis_queries/query6.cypher
@@ -0,0 +1,5 @@
 // Top 10 Drugs treating multiple Diseases
 MATCH (c:Compound)-[:TREATS]->(d:Disease)
 RETURN c.name AS Drug, count(d) AS DiseaseCount
 ORDER BY DiseaseCount DESC
 LIMIT 10;
--- a/neo4jqueries/analysis_queries/query7.cypher
+++ b/neo4jqueries/analysis_queries/query7.cypher
@@ -0,0 +1,7 @@
 // Drugs treating diseases connected to the same genes
 MATCH (g:Gene)<-[:ASSOCIATES]-(d1:Disease)<-[:TREATS]-(c:Compound),
      (g)<-[:ASSOCIATES]-(d2:Disease)
 WHERE NOT (c)-[:TREATS]->(d2)
 RETURN c.name AS Drug, d2.name AS CandidateDisease, count(g) AS SharedGenes
 ORDER BY SharedGenes DESC, Drug
 LIMIT 20;
--- a/neo4jqueries/analysis_queries/query8.cypher
+++ b/neo4jqueries/analysis_queries/query8.cypher
@@ -0,0 +1,4 @@
 // LOOK for 999 Treatments for diseases
 MATCH p=()-[:TREATS]->() 
 RETURN p 
 LIMIT 999;
--- a/neo4jqueries/loadingQueriesNeo4j/LoadNodes.cypher
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadNodes.cypher
@@ -0,0 +1,19 @@
 // Genes
 LOAD CSV WITH HEADERS FROM 'file:///nodes_Gene.csv' AS row
 CREATE (g:Gene) SET g = row;
 // Diseases
 LOAD CSV WITH HEADERS FROM 'file:///nodes_Disease.csv' AS row
 CREATE (d:Disease) SET d = row;
 // Compounds
 LOAD CSV WITH HEADERS FROM 'file:///nodes_Compound.csv' AS row
 CREATE (c:Compound) SET c = row;
 // Symptoms
 LOAD CSV WITH HEADERS FROM 'file:///nodes_Symptom.csv' AS row
 CREATE (s:Symptom) SET s = row;
 // Side Effects
 LOAD CSV WITH HEADERS FROM 'file:///nodes_Side_Effect.csv' AS row
 CREATE (se:Side_Effect) SET se = row;
--- a/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Associates
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Associates
@@ -0,0 +1,4 @@
 LOAD CSV WITH HEADERS FROM 'file:///edges_treats.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
 CREATE (source)-[:TREATS]->(target);
--- a/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Binds.cypher
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Binds.cypher
@@ -0,0 +1,4 @@
 LOAD CSV WITH HEADERS FROM 'file:///edges_binds.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
 CREATE (source)-[:BINDS]->(target);
--- a/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Causes
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Causes
@@ -0,0 +1,4 @@
 LOAD CSV WITH HEADERS FROM 'file:///edges_interacts.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
 CREATE (source)-[:INTERACTS]->(target);
--- a/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Covaries.cypher
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Covaries.cypher
@@ -0,0 +1,4 @@
 LOAD CSV WITH HEADERS FROM 'file:///edges_covaries.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
 CREATE (source)-[:COVARIES]->(target);
--- a/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Interacts.cypher
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Interacts.cypher
@@ -0,0 +1,4 @@
 LOAD CSV WITH HEADERS FROM 'file:///edges_causes.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
 CREATE (source)-[:CAUSES]->(target);
--- a/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Upregulates.cypher
+++ b/neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Upregulates.cypher
@@ -0,0 +1,4 @@
 LOAD CSV WITH HEADERS FROM 'file:///edges_causes.csv' AS row
 MATCH (source {id: row.source})            
 MATCH (target {id: row.target})
 CREATE (source)-[:CAUSES]->(target);