Update README.md

README.md

@@ -8,13 +8,12 @@ size_categories:
 
 Pep2Prob is a comprehensive dataset designed to predict peptide-specific fragment ion probability in tandem mass spectrometry (MS/MS) based proteomics studies. This dataset addresses the limitations of conventional global statistical approaches by enabling the development of models that can predict fragmentation probabilities based on peptide sequence context.
 
-## 📊 Dataset Overview
+## 📊 Dataset Overview and Highlights
 
-The dataset provides:
-- **Fragment ion probability statistics for 610,117 unique peptide precursors** derived from over 183 million high-resolution HCD spectra
+- The dataset provides **Fragment ion probability statistics for 610,117 unique peptide precursors** derived from over 183 million high-resolution HCD spectra
 - Diverse representation of precursors with varying lengths (7-40 amino acids) and charge states (1-8)
 - High-quality annotations derived from validated peptide assignments with 0.1% false discovery rate
-- Comprehensive coverage of up to 235 fragment ions per precursor
+- A novel train-test split scheme is adapted to minimize structural similarity between entries in the training and the testing set.
 
 ## 🔗 Code and Documentation
 
@@ -24,7 +23,8 @@ The dataset provides:
 
 ## ⚠️ Important Data Access Notice
 
-**HuggingFace Statistics Issue**: The repository statistics incorrectly show ~3.15 million entries due to a platform counting error. HuggingFace sums across our five cross-validation splits (610,117 × 5 ≈ 3.15M) instead of recognizing these as the same unique precursors distributed into separate folds.
+<!-- **HuggingFace Statistics Issue**: -->
+> The repository statistics incorrectly show ~3.15 million entries due to a platform counting error. HuggingFace sums across our five train-test splits (610,117 × 5 ≈ 3.15M) instead of recognizing these as the same unique precursors distributed into separate folds.
 
 **Recommended Data Access Methods**:
 
@@ -47,19 +47,19 @@ Pep2Prob/
 ├── meta_data/            
 │   ├── X_columns.parquet                   # Input feature metadata
 │   └── Y_columns.parquet                   # Target variable metadata
-├── train_test_split_set_1/                 # Cross-validation split 1
+├── train_test_split_set_1/                 # Train-test split 1
 │   ├── train_indices.parquet
 │   └── test_indices.parquet
-├── train_test_split_set_2/                 # Cross-validation split 2
+├── train_test_split_set_2/                 # Train-test split 2
 │   ├── train_indices.parquet
 │   └── test_indices.parquet
-├── train_test_split_set_3/                 # Cross-validation split 3
+├── train_test_split_set_3/                 # Train-test split 3
 │   ├── train_indices.parquet
 │   └── test_indices.parquet
-├── train_test_split_set_4/                 # Cross-validation split 4
+├── train_test_split_set_4/                 # Train-test split 4
 │   ├── train_indices.parquet
 │   └── test_indices.parquet
-└── train_test_split_set_5/                 # Cross-validation split 5
+└── train_test_split_set_5/                 # Train-test split 5
     ├── train_indices.parquet
     └── test_indices.parquet
 ```
@@ -71,7 +71,7 @@ Pep2Prob/
 - **`Y_columns.csv`**: Metadata describing target variables (probability values for different fragment ion types)
 - **`train_test_split_set_X/`**: Five pre-defined cross-validation splits ensuring no sequence similarity between training and testing sets, preventing data leakage
 
-## 🎯 Cross-Validation Methodology
+## 🎯 Train-test split Methodology
 
 Our dataset uses a sophisticated sequence-similarity-aware splitting strategy:
 
@@ -115,6 +115,6 @@ We welcome contributions! Please see our [GitHub repository](https://github.com/
 - Benchmark improvements and new baseline models
 - Documentation enhancements
 
-## ⭐ Acknowledgments
+<!-- ## ⭐ Acknowledgments -->
 
-This dataset was constructed from publicly available mass spectrometry data in the MassIVE repository, with curation based on the MassIVE Knowledge Base. We thank the proteomics community for sharing high-quality data that enables this research.
+<!-- This dataset was constructed from publicly available mass spectrometry data in the MassIVE repository, with curation based on the MassIVE Knowledge Base. We thank the proteomics community for sharing high-quality data that enables this research. -->