A multi-dimensional network-based approach to elucidate the molecular mechanism behind tuberculosis and HIV co-infection.

Key words: TB/HIV coinfection, Multi-dimensional graphs, Systems biology, predictive modeling, drug discovery

Description: This project aims to elucidate the molecular mechanisms underlying TB/HIV coinfection through multi-dimensional graph-based modeling. The extracted information from this modeling will be utilized for biomarker identification, QSAR modeling, and virtual screening efforts. Additionally, the project seeks to understand the impact of environmental toxicology on disease progression and occurrence.

Attached files: 

1. Figures 1-14 included in the manuscript
2. Tables 1-4 and S1-S3 in one file
3. Figure S1-S11 supplementary images
4. Dataset: i.)TB/HIV biological knowledge graph, ii.) Marker/Mechanistic chemicals and AOPs network
5. Knime workflow: i.)Dataset mapping and curation, ii). QSAR models for TNF, IL1B & IFNG, iii.) AQI_TB/HIV co-occurrence predictive modeling 
6. Python script: Contains codes for i.) JI similarity calculation, ii.)Single and Multi-dimensional Hierarchical clustergrams, iii.) Pubmed Text analysis, iv.) chemical similarity analysis