finishes setup

Dockerfile

@@ -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"]
+

README.md

@@ -8,12 +8,12 @@ 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
 
@@ -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
-
-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
+### 2. 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
-2. **Machine Learning**: Predictive models for drug efficacy
-3. **Temporal Analysis**: Track knowledge graph changes over time
-4. **API Development**: REST API for programmatic access
-5. **Cloud Deployment**: AWS/GCP hosting for web access
-6. **Additional Data Sources**: Integrate DrugBank, KEGG, etc.
+1. **Machine Learning**: Predictive models for drug efficacy
+2. **Temporal Analysis**: Track knowledge graph changes over time
+3. **API Development**: REST API for programmatic access
+4. **Cloud Deployment**: AWS/GCP hosting for web access
+5. **Additional Data Sources**: Integrate DrugBank, KEGG, etc.
 
 ## References
 

dashboard.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")

etl.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
@@ -153,22 +110,43 @@ 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"Saved to: {edges_file.name}")
+print(f"\nSummary:")
+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
-print("\n Pre-filtering edges by type...")
+# Pre-filter edges by type for analysis
+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:
-        print(f"   - {edge_type}: {len(edges_by_type[edge_type]):,}")
+    count = len(edges_by_type[edge_type])
+    pct = 100 * count / len(edges_df)
+    print(f"   - {edge_type:20s}: {count:>10,} ({pct:>5.1f}%)")
 
-
-# ANALYSES
+# [ANALYSES - keeping all the existing analysis code...]
+# (Keeping the same analysis code as before)
 
 print("\n" + "="*60)
 print("PHASE 4: ANALYSES")
@@ -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)
@@ -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)
 
-
-
-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"Created network with {len(network_nodes_df):,} nodes and {len(network_edges_df):,} edges")
 
 
 
-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()}")

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
-Metoprolol,1,181,0.005494505494505495
+Pentostatin,1,179,0.005555555555555556
 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

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
-Fatigue,25,137,131,3425
-Psychophysiologic Disorders,18,170,160,3060
-Birth Weight,14,168,159,2352
-Paresthesia,26,87,83,2262
+Edema,49,216,204,10584
+Body Weight,23,169,159,3887
+Fever of Unknown Origin,25,107,98,2675
+Neurologic Manifestations,28,84,79,2352
 "Hearing Loss, Sensorineural",24,70,65,1680
-Dyspnea,21,77,75,1617
-Cerebellar Ataxia,17,84,82,1428
-Cough,16,86,83,1376
-"Purpura, Thrombocytopenic, Idiopathic",15,89,83,1335
-Muscular Atrophy,16,81,77,1296
-Sleep Apnea Syndromes,12,95,90,1140
-Ataxia,16,68,67,1088
-Intellectual Disability,17,59,58,1003
-"Purpura, Thrombocytopenic",14,70,64,980
-Flushing,12,75,73,900
-Asthenia,13,68,66,884
-"Obesity, Abdominal",8,108,103,864
-Sciatica,15,50,48,750
-Aphasia,13,57,57,741
-Respiratory Sounds,11,67,66,737
-Ataxia Telangiectasia,13,49,47,637
-Vertigo,13,48,46,624
-"Urinary Bladder, Neurogenic",16,37,36,592
-"Purpura, Thrombotic Thrombocytopenic",7,84,81,588
-Syncope,11,48,47,528
+Constipation,21,75,70,1575
+Polyuria,12,101,96,1212
+Facial Pain,17,66,63,1122
+Sensation Disorders,16,68,64,1088
+Facial Paralysis,19,56,53,1064
+Quadriplegia,17,61,59,1037
+Oral Manifestations,15,62,57,930
+Pelvic Pain,17,49,47,833
+Hearing Disorders,18,46,45,828
+Hemoptysis,16,50,49,800
+Angina Pectoris,8,85,80,680
+Scotoma,12,55,54,660
+Weight Loss,9,62,60,558
+Horner Syndrome,13,42,41,546
+Hearing Loss,15,36,35,540
+Torticollis,14,37,35,518
 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
-Cerebrospinal Fluid Rhinorrhea,10,39,38,390
-Eye Hemorrhage,8,47,44,376
-"Hearing Loss, High-Frequency",10,37,36,370
+Dysphonia,8,55,53,440
+"Jaundice, Obstructive",14,30,29,420
+Mobility Limitation,9,44,42,396
+Pupil Disorders,10,37,37,370
 Chronic Pain,8,46,44,368
-Olfaction Disorders,10,36,36,360
-"Urinary Bladder, Overactive",6,59,58,354
-Color Vision Defects,9,39,39,351
-Acute Coronary Syndrome,6,47,46,282
-Pruritus Ani,6,43,39,258
-Eye Pain,7,36,35,252
+"Purpura, Schoenlein-Henoch",10,36,32,360
+Dystonia,9,37,37,333
+"Gait Disorders, Neurologic",11,26,25,286
+Tonic Pupil,5,57,56,285
+Vitreous Hemorrhage,8,34,33,272
+Agraphia,8,32,32,256
 Agnosia,7,36,36,252
-"Hearing Loss, Bilateral",8,30,29,240
-"Hearing Loss, Noise-Induced",3,80,74,240
-"Diarrhea, Infantile",7,34,28,238
+Eye Pain,7,36,35,252
+Perceptual Disorders,11,22,22,242
 Tremor,5,46,45,230
-Choroid Hemorrhage,6,34,33,204
-Sarcopenia,5,39,38,195
-"Hearing Loss, Central",6,32,31,192
-Hiccup,7,27,27,189
+"Aging, Premature",6,38,36,228
+"Sleep Apnea, Central",5,45,45,225
+Tinea Pedis,5,43,41,215
+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
-Miosis,4,27,27,108
-Athetosis,3,36,36,108
-"Purpura, Hyperglobulinemic",4,24,20,96
-Metatarsalgia,3,27,25,81
-Toothache,5,16,16,80
-Ageusia,3,26,24,78
-Rett Syndrome,3,25,25,75
+Catalepsy,5,25,25,125
+Chills,3,39,35,117
+Pseudobulbar Palsy,3,37,37,111
+Photophobia,4,27,27,108
+Seizures,3,32,32,96
+Pruritus Vulvae,3,30,28,90
+Sneezing,2,43,42,86
+"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
-Dyssomnias,2,29,28,58
-Hyperemesis Gravidarum,3,18,18,54
+"Paraparesis, Spastic",4,16,16,64
+"Amnesia, Transient Global",2,29,29,58
 Respiratory Paralysis,4,13,13,52
-Nocturnal Paroxysmal Dystonia,2,25,25,50
-Hyperesthesia,3,14,13,42
+Muscle Spasticity,4,12,12,48
+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
-Urinoma,3,12,12,36
-Eructation,2,15,13,30
-Ophthalmoplegic Migraine,2,14,14,28
-Sleep Arousal Disorders,1,25,25,25
+Breakthrough Pain,2,18,18,36
+Neuroacanthocytosis,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
-Halitosis,4,4,4,16
-"Apraxia, Ideomotor",3,5,5,15
+WAGR Syndrome,1,13,13,13
 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
-Anhedonia,4,1,1,4
-Striae Distensae,1,2,2,2
-Fetal Weight,2,1,1,2
-Wolfram Syndrome,1,1,1,1
-"Dyskinesia, Drug-Induced",5,0,0,0
-Nocturnal Myoclonus Syndrome,5,0,0,0
+Alice in Wonderland Syndrome,1,7,7,7
+Usher Syndromes,2,2,2,4
+Waterhouse-Friderichsen Syndrome,1,0,0,0
+"Polydipsia, Psychogenic",1,0,0,0
+Gait Apraxia,1,0,0,0
+Machado-Joseph Disease,2,0,0,0
+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

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
+DB00328,DOID:7147
+DB00218,DOID:3083
+DB00989,DOID:14330
+DB00203,DOID:418
+DB00500,DOID:8398
+DB01224,DOID:12849
+DB01104,DOID:5419
+DB00996,DOID:9074
+DB01175,DOID:1595
+DB00104,DOID:4989
+DB00741,DOID:263
+DB01267,DOID:5419
+DB01068,DOID:14330
+DB01200,DOID:14330
+DB00692,DOID:10763
+DB00402,DOID:2377
+DB00810,DOID:14330
+DB01063,DOID:5419
+DB01255,DOID:1094
+DB00215,DOID:1595
+DB00230,DOID:9074
+DB00500,DOID:7148
+DB00980,DOID:14330
+DB00281,DOID:3393
+DB00404,DOID:594
+DB00402,DOID:5419
+DB01001,DOID:4481
+DB00985,DOID:5419
+DB01224,DOID:3312
+DB00829,DOID:594
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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.")

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;

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;

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;

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;

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;

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;

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;

neo4jqueries/analysis_queries/query8.cypher

@@ -0,0 +1,4 @@
+// LOOK for 999 Treatments for diseases
+MATCH p=()-[:TREATS]->() 
+RETURN p 
+LIMIT 999;

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;

neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Associates copy.cypher

@@ -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);

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);

neo4jqueries/loadingQueriesNeo4j/LoadRelationshipsEdges_Causes copy 2.cypher

@@ -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);

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);

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);

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);