# yyyuechen/ai-omics-for-cancer-regulatory-gene-identification architecture

> Architecture diagram of the GitHub repository yyyuechen/ai-omics-for-cancer-regulatory-gene-identification, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification
- Repository: https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification

## Overview

This repository is a reproduction workflow for finding and interpreting candidate regulatory genes in aflatoxin B1-induced hepatocellular carcinoma. Its main path runs from GEO expression preprocessing and target collection through differential expression, co-expression and network analysis, then machine-learning selection and signature validation. Molecular docking is a distinct analysis branch. The README documents these stages and their order; source excerpts were unavailable, so links represent documented workflow handoffs rather than verified function calls.

Main entry points: [`scripts/05_PPI_and_enrichment_analysis.R`](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/05_PPI_and_enrichment_analysis.R) (PPI and enrichment), [`scripts/01_HCCdata_preparation.R`](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/01_HCCdata_preparation.R) (Expression preprocessing), [`scripts/03_batch_correction_and_DEG_analysis.R`](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/03_batch_correction_and_DEG_analysis.R) (Batch correction and DEGs), [`scripts/04_WGCNA_module_analysis_and_DEG_integration.R`](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/04_WGCNA_module_analysis_and_DEG_integration.R) (WGCNA and DEG integration)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_transcriptomics["Transcriptomics"]
  node_prep["Expression preprocessing"]
  node_deg["Batch correction and DEGs"]
  node_wgcna["WGCNA and DEG integration"]
end

subgraph group_network["Candidate Networks"]
  node_targets["Target integration"]
  node_ppi["PPI and enrichment"]
  node_candidate_genes["Integrated candidates"]
end

subgraph group_machine_learning["Machine Learning"]
  node_feature_prep["Cohort preparation and Lasso-RF"]
  node_model_compare["Model comparison"]
  node_signature["Signature validation and interpretation"]
end

subgraph group_docking["Molecular Docking"]
  node_docking["Docking workflow"]
end

node_researcher(("Researcher"))
node_geo[("GEO datasets")]
node_target_sources[("Target resources")]
node_docking_resources[("Structure and ligand resources")]
node_docking_tools["Docking and visualization tools"]

node_researcher -->|"runs"| node_prep
node_geo -->|"provides datasets"| node_prep
node_prep -->|"provides expression data"| node_deg
node_researcher -->|"runs"| node_targets
node_target_sources -->|"provides target candidates"| node_targets
node_researcher -->|"runs"| node_deg
node_deg -->|"provides DEG results"| node_wgcna
node_researcher -->|"runs"| node_wgcna
node_wgcna -->|"provides integrated candidates"| node_ppi
node_targets -->|"provides AFB1 candidates"| node_ppi
node_researcher -->|"runs"| node_ppi
node_ppi -->|"produces"| node_candidate_genes
node_candidate_genes -->|"provides candidate genes"| node_feature_prep
node_researcher -->|"runs"| node_feature_prep
node_feature_prep -->|"provides cohort data"| node_model_compare
node_researcher -->|"runs"| node_model_compare
node_model_compare -->|"informs signature validation"| node_signature
node_researcher -->|"runs"| node_signature
node_researcher -->|"follows tutorial"| node_docking
node_docking_resources -->|"provides structures and ligand"| node_docking
node_docking -->|"uses"| node_docking_tools

click node_prep "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/01_HCCdata_preparation.R"
click node_targets "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/02_target_database_integration.R"
click node_deg "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/03_batch_correction_and_DEG_analysis.R"
click node_wgcna "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/04_WGCNA_module_analysis_and_DEG_integration.R"
click node_ppi "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/05_PPI_and_enrichment_analysis.R"
click node_candidate_genes "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/05_PPI_and_enrichment_analysis.R"
click node_feature_prep "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/06_data_preparation_and_LassoRF.R"
click node_model_compare "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/07_machine_learning_model_comparison.R"
click node_signature "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/08_final_signature_model_validation_and_interpretation.R"
click node_docking "https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/09_Molecular_docking_analysis.txt"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_prep,node_deg,node_wgcna toneBlue
class node_targets,node_ppi,node_candidate_genes,node_geo,node_target_sources,node_docking_resources toneAmber
class node_feature_prep,node_model_compare,node_signature toneMint
class node_docking toneRose
class node_researcher,node_docking_tools toneIndigo
```

## Components

### Transcriptomics

- [Expression preprocessing](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/01_HCCdata_preparation.R): `scripts/01_HCCdata_preparation.R`
- [Batch correction and DEGs](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/03_batch_correction_and_DEG_analysis.R): `scripts/03_batch_correction_and_DEG_analysis.R`
- [WGCNA and DEG integration](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/04_WGCNA_module_analysis_and_DEG_integration.R): `scripts/04_WGCNA_module_analysis_and_DEG_integration.R`

### Candidate Networks

- [Target integration](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/02_target_database_integration.R): `scripts/02_target_database_integration.R`
- [PPI and enrichment](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/05_PPI_and_enrichment_analysis.R): `scripts/05_PPI_and_enrichment_analysis.R`
- [Integrated candidates](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/05_PPI_and_enrichment_analysis.R): `scripts/05_PPI_and_enrichment_analysis.R`

### Machine Learning

- [Cohort preparation and Lasso-RF](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/06_data_preparation_and_LassoRF.R): `scripts/06_data_preparation_and_LassoRF.R`
- [Model comparison](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/07_machine_learning_model_comparison.R): `scripts/07_machine_learning_model_comparison.R`
- [Signature validation and interpretation](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/08_final_signature_model_validation_and_interpretation.R): `scripts/08_final_signature_model_validation_and_interpretation.R`

### Molecular Docking

- [Docking workflow](https://github.com/yyyuechen/ai-omics-for-cancer-regulatory-gene-identification/blob/main/scripts/09_Molecular_docking_analysis.txt): `scripts/09_Molecular_docking_analysis.txt`

### Other components

- **Researcher**
- **GEO datasets**
- **Target resources**
- **Structure and ligand resources**
- **Docking and visualization tools**

## Connections

- Researcher → Expression preprocessing: runs
- GEO datasets → Expression preprocessing: provides datasets
- Expression preprocessing → Batch correction and DEGs: provides expression data
- Researcher → Target integration: runs
- Target resources → Target integration: provides target candidates
- Researcher → Batch correction and DEGs: runs
- Batch correction and DEGs → WGCNA and DEG integration: provides DEG results
- Researcher → WGCNA and DEG integration: runs
- WGCNA and DEG integration → PPI and enrichment: provides integrated candidates
- Target integration → PPI and enrichment: provides AFB1 candidates
- Researcher → PPI and enrichment: runs
- PPI and enrichment → Integrated candidates: produces
- Integrated candidates → Cohort preparation and Lasso-RF: provides candidate genes
- Researcher → Cohort preparation and Lasso-RF: runs
- Cohort preparation and Lasso-RF → Model comparison: provides cohort data
- Researcher → Model comparison: runs
- Model comparison → Signature validation and interpretation: informs signature validation
- Researcher → Signature validation and interpretation: runs
- Researcher → Docking workflow: follows tutorial
- Structure and ligand resources → Docking workflow: provides structures and ligand
- Docking workflow → Docking and visualization tools: uses