https://github.com/scai-bio/covid-ndd-comorbidity-nlp
This work aims to integrate diverse, curated biological databases with a comprehensive analysis of the growing body of literature on the potential long-term neurological consequences of COVID-19 infections, employing advanced natural language processing and text mining techniques.
https://github.com/scai-bio/covid-ndd-comorbidity-nlp
Last synced: about 2 months ago
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This work aims to integrate diverse, curated biological databases with a comprehensive analysis of the growing body of literature on the potential long-term neurological consequences of COVID-19 infections, employing advanced natural language processing and text mining techniques.
- Host: GitHub
- URL: https://github.com/scai-bio/covid-ndd-comorbidity-nlp
- Owner: SCAI-BIO
- Created: 2024-08-12T09:14:08.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-03T09:04:30.000Z (4 months ago)
- Last Synced: 2025-02-03T09:38:57.442Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 3.27 MB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Exploring the Current State of Knowledge on the Link Between COVID-19 and Neurodegeneration
This repository contains the data, scripts, and analyses used in the research titled **"Unravelling the Co-Morbidity between COVID-19 and Neurodegenerative Diseases Through Multi-scale Graph Analysis: A Systematic investigation of Biological Databases and Text Mining"**. The project leverages Neo4j paltform for graph-based analysis and integrates natural language processing to explore relationships between COVID-19 and neurodegenerative diseases (NDDs). The constructed comorbidity database is publicly available in Aura database, with credentials provided below.
## Table of Contents
- [Overview](#overview)
- [Data](#data)
- [Sources](#sources)
- [Notebooks](#notebooks)
- [Getting Started](#getting-started)
- [Exploring the Covid-NDD Comorbidity Database](#Exploring-the-Covid-NDD-Comorbidity-Database)
- [Contact](#contact)
## Overview
This project explores the connections between COVID-19 and neurodegenerative diseases by:
1. **Integrating database information** about COVID-19 and NDDs and storing them in a graph structure.
2. **Extracting textual data** from scientific literature and using natural language processing pipelines for information extraction and KG construction.
3. **Loading all KG** in Neo4j to identify and analyse relationships and pathways between entities such as genes, diseases, and chemicals.
4. **Construction of a hypothesis database for omorbidity between COVID-19 and NDDs** to explore, analyse, and visualise testable comorbidity hypotheses.
## Data
The repository includes the following directories:
1. **Expert-curated-publications**: Contains manually curated publications relevant to the study, ensuring high-quality and accurate information.
2. **PubTator3-results**: Includes results from PubTator3, a web-based system that offers a comprehensive set of features and tools for exploring biomedical literature using advanced text mining and AI techniques.
3. **Sherpa-results**: Houses outputs from Sherpa, a tool designed to assist in the curation of biomedical literature by providing automated annotations and insights.
4. **Textual-corpora-for-textmining**: Comprises textual corpora prepared for text mining purposes, facilitating the extraction of meaningful patterns and relationships regarding COVID-19 and NDD.
5. **comorbidity_paths.txt**: Some example comorbidity pathways.
6. **hypothesis_pmid_evidences.csv**: Some of the comorbidity triples with source evidence uploaded to the databases.
## Sources
### 1. `comorbidity-hypothesis-db.py`
- **Purpose**: Automatically opens the Neo4j Browser with prefilled credentials to connect to the AuraDB instance for comorbidity hypothesis exploration.
- **Key Features**:
- Simplifies connection to Neo4j by generating a pre-configured URL.
- Useful for direct interaction with the knowledge graph.
- **Usage**:
Run the script, and the Neo4j Browser will open in your default web browser:
```bash
python comorbidity-hypothesis-db.py
### 2. `comorbidity_database_neo4j_upload.py`
- **Purpose**: Uploads curated comorbidity hypothesis paths to the Neo4j AuraDB instance.
- **Key Features**:
- Simplifies uploading comorbidity hypothesis candidates.
- Standardizes and normalizes graph entities for compatibility.
- **Usage**:
Run the script to upload the data:
```bash
python comorbidity_database_neo4j_upload.py
```
## Getting Started
### Prerequisites
- **Neo4j AuraDB**: Ensure you have access to a Neo4j AuraDB instance. Use the provided connection details or set up your own.
- **Python Environment**: Install the required libraries:
```bash
pip install neo4j pandas
## Notebooks
### 1. `analyze-neo4j.ipynb`
- **Purpose**: Analyzes the knowledge graph loaded to Neo4j to extract insights.
- **Key Features**:
- Counts nodes and edges in the graph.
- Executes community detection algorithms like Louvain using Neo4j's Graph Data Science (GDS) library.
- Retrieves and visualizes properties of detected clusters
- **Usage**:
Open the Jupyter Notebook and follow the instructions to:
- Query the Neo4j database.
- Get general statistics about nodes, triple and pathways, and analyze them.
## Exploring the Covid-NDD Comorbidity Database
To manually explore the comorbidity graph database:
1. **Open the Neo4j Browser**:
Navigate to [https://browser.neo4j.io](https://browser.neo4j.io).
2. **Enter the Connection Details**:
- **URI**: `neo4j+s://09f8d4e9.databases.neo4j.io`
- **Username**: `neo4j`
- **Password**: Refer to the credentials provided in the [**src/comorbidity-hypothesis-db.py**](https://github.com/SCAI-BIO/covid-NDD-comorbidity-NLP/blob/main/src/comorbidity-hypothesis-db.py).
3. **Run Cypher Queries**:
Once connected, you can execute Cypher queries to explore the graph. For example, to retrieve a sample of nodes:
```cypher
MATCH (n) RETURN n LIMIT 10;
## Contact
For any questions, suggestions, or collaborations, please contact:
**Negin Babaiha**
Email: [[email protected]](mailto:[email protected])
[Google Scholar Profile](https://scholar.google.com/citations?user=OwT3AMQAAAAJ)
Feel free to reach out for discussions regarding the project!