Update README.md

README.md

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 license: mit
 task_categories:
 - feature-extraction
+- token-classification
+- zero-shot-classification
 language:
 - en
 tags:
@@ -9,4 +11,170 @@ tags:
 - medical
 size_categories:
 - 100B<n<1T
----
+---
+
+# Epigenomic Data for ME/CFS and Long COVID Classification
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+## Dataset Overview
+
+This dataset contains DNA methylation data from blood samples of individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), Long COVID (LC), and healthy controls. The data is derived from Illumina HumanMethylation450 BeadChip and Illumina MethylationEPIC arrays and has been organized to facilitate research into epigenetic biomarkers for these conditions.
+
+## Dataset Structure
+
+```
+root/
+├── ME/                   # Myalgic Encephalomyelitis/Chronic Fatigue Syndrome samples
+│   ├── GSE102504/        # GEO Series accession number
+│   │   └── GSE102504_RAW/    # Raw data directory
+│   │       └── *.idat    # Illumina IDAT files
+│   ├── GSE111183/
+│   │   └── GSE111183_RAW/
+│   │       └── *.idat
+│   ├── GSE112905/
+│   │   └── GSE112905_RAW/
+│   │       └── *.idat
+│   ├── GSE153667/
+│   │   └── GSE153667_RAW/
+│   │       └── *.idat
+│   ├── GSE156792/
+│   │   └── GSE156792_RAW/
+│   │       └── *.idat
+│   ├── GSE166592/
+│   │   └── GSE166592_RAW/
+│   │       └── *.idat
+│   ├── GSE59489/
+│   │   └── GSE59489_RAW/
+│   │       └── *.idat
+│   └── GSE93266/
+│       └── GSE93266_RAW/
+│           └── *.idat
+├── LC/                   # Long COVID samples
+│   ├── GSE161678/
+│   │   └── GSE161678_RAW/
+│   │       └── *.idat
+│   ├── GSE174818/
+│   │   └── GSE174818_RAW/
+│   │       └── *.idat
+│   ├── GSE188573/
+│   │   └── GSE188573_RAW/
+│   │       └── *.idat
+│   ├── GSE197152/
+│   │   └── GSE197152_RAW/
+│   │       └── *.idat
+│   └── GSE210430/
+│       └── GSE210430_RAW/
+│           └── *.idat
+└── controls/             # Healthy control samples
+    ├── GSE112905/
+    │   └── GSE112905_RAW/
+    │       └── *.idat
+    ├── GSE40279/
+    │   └── GSE40279_RAW/
+    │       └── *.idat
+    └── GSE42861/
+        └── GSE42861_RAW/
+            └── *.idat
+```
+
+## Data Description
+
+- **File Format**: `.idat` (Illumina's proprietary binary file format for BeadArray data)
+- **Platforms**: Illumina HumanMethylation450 BeadChip and Illumina MethylationEPIC arrays
+- **Sample Composition**: Approximately 30 ME/CFS patients, 20 Long COVID patients, and 20 healthy controls
+- **Tissue Type**: Peripheral blood
+- **Data Type**: DNA methylation beta values (representing the ratio of methylated probe intensity to total probe intensity)
+
+## About the Conditions
+
+### ME/CFS (Myalgic Encephalomyelitis/Chronic Fatigue Syndrome)
+- Chronic multi-system illness affecting approximately 0.5-1% of the population worldwide
+- Often triggered by viral illnesses
+- Characterized by long-term fatigue, post-exertional malaise, cognitive impairment, and autonomic dysfunction
+- Currently diagnosed by clinical exclusion due to lack of definitive biomarkers
+
+### Long COVID (Post-Acute Sequelae of COVID-19)
+- Refers to prolonged symptoms after acute SARS-CoV-2 infection
+- Affects approximately 10-20% of COVID-19 survivors
+- Shares some clinical features with ME/CFS
+- Similarly lacks definitive biomarkers and is diagnosed primarily by clinical assessment
+
+## Data Processing Guidelines
+
+To process this methylation data, we recommend the following pipeline:
+
+1. **Quality Control**:
+   - Filter out probes on sex chromosomes
+   - Remove known polymorphic or cross-reactive probes
+   - Perform sample quality checks (detection p-values, intensity distributions)
+
+2. **Normalization**:
+   - Beta-value calculation
+   - BMIQ normalization for type I/II bias correction
+   - Optional: ComBat batch correction if combining multiple datasets
+
+3. **Feature Selection**:
+   - Differential methylation analysis to identify disease-associated CpG sites
+   - Optional: Variance filtering to remove low-variability probes
+
+4. **Analysis**:
+   - Recommended: Apply the Epigenomic Transformer Pipeline or similar methods
+   - Alternative approaches: Logistic regression, random forest, XGBoost
+
+## Usage Example
+
+Here's a simple example of how to load and process this data using the `minfi` package in R:
+
+```r
+library(minfi)
+library(limma)
+library(wateRmelon)
+
+# Load raw data
+baseDir <- "path/to/dataset/ME/GSE111183/GSE111183_RAW"
+targets <- read.metharray.sheet(baseDir)
+rgSet <- read.metharray.exp(targets=targets)
+
+# Preprocess
+mSet <- preprocessRaw(rgSet)
+beta <- getBeta(mSet)
+
+# Normalization
+beta_norm <- BMIQ(beta)
+
+# Visualization example
+densityPlot(beta_norm, sampGroups=targets$Group)
+```
+
+## Research Applications
+
+This dataset is suitable for:
+
+1. Developing and validating diagnostic classifiers for ME/CFS and Long COVID
+2. Identifying epigenetic biomarkers specific to each condition
+3. Studying the biological mechanisms underlying these post-viral illnesses
+4. Comparing epigenetic patterns between ME/CFS and Long COVID
+5. Training machine learning models for disease classification
+
+## Citation Information
+
+If you use this dataset in your research, please cite:
+
+```
+Acharya, P., & Jacoby, D. (2025). Epigenomic Transformer Pipeline for ME/CFS and Long COVID Classification. 
+University of Victoria.
+```
+
+## Additional Resources
+
+For more information about the Epigenomic Transformer Pipeline developed using this data, please visit our [GitHub repository](https://github.com/your-repo-link).
+
+## Acknowledgements
+
+Data was sourced from the NCBI Gene Expression Omnibus (GEO) with the following accession numbers:
+- ME/CFS data: GSE102504, GSE111183, GSE112905, GSE153667, GSE156792, GSE166592, GSE59489, GSE93266
+- Long COVID data: GSE161678, GSE174818, GSE188573, GSE197152, GSE210430
+- Control data: GSE112905, GSE40279, GSE42861
+
+We thank all the researchers who contributed to generating and sharing this valuable data.