Update README.md

README.md

@@ -13,7 +13,7 @@ size_categories:
 - 100B<n<1T
 ---
 
-# Epigenomic Data for ME/CFS and Long COVID Classification
+# Transformer Attention Heads in Epigenetics of ME/CFS and Long COVID
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
@@ -100,28 +100,6 @@ root/
 - 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:
@@ -147,16 +125,6 @@ beta_norm <- BMIQ(beta)
 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:
@@ -168,7 +136,7 @@ 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).
+For more information about the Epigenomic Transformer Pipeline developed using this data, please visit our [GitHub repository](https:github.com/VerisimilitudeX/EpiMECoV).
 
 ## Acknowledgements