Update README.md

README.md

@@ -20,19 +20,21 @@ The model is trained with a loss function that combines classification loss and
 
 ## Intended uses
 
-`PDeepPP` is designed for two primary tasks:
+`PDeepPP` was developed and validated using PTM and BPS datasets, but its applications are not limited to these specific tasks. Leveraging its flexible architecture and robust feature extraction capabilities, `PDeepPP` can be applied to a wide range of protein sequence-related analysis tasks. Specifically, the model has been validated on the following datasets:
 
-1. **PTM site prediction**: Identifying post-translational modification sites (e.g., phosphorylation) in protein sequences, focusing on serine (S), threonine (T), and tyrosine (Y) residues.
-2. **Biologically active sequence analysis (BPS)**: Extracting biologically relevant regions from protein sequences for downstream analysis.
+1. **PTM datasets**: Used for predicting post-translational modification (PTM) sites (e.g., phosphorylation), focusing on serine (S), threonine (T), and tyrosine (Y) residues.
+2. **BPS datasets**: Used for analyzing biologically active regions of protein sequences (Biologically Active Protein Sequences, BPS) to support downstream analyses.
 
-The model processes protein sequences and outputs:
+Although the model was trained and validated on PTM and BPS datasets, `PDeepPP`’s architecture enables users to generalize and extend its capabilities to other protein sequence analysis tasks, such as embedding generation, sequence classification, or task-specific analyses.
 
-- Embedded representations of the sequences, which can be used for various downstream tasks.
-- Predicted probabilities for PTM or other sequence-specific features.
+---
+
+### Key features
 
-### Key features:
-- **PTM mode**: Focuses on sequences centered around specific residues (S, T, Y) to predict PTM activity.
-- **BPS mode**: Analyzes overlapping or non-overlapping subsequences of a protein for broader biological insights.
+- **Dataset support**: `PDeepPP` is trained on PTM and BPS datasets, demonstrating its effectiveness in identifying specific sequence features (e.g., post-translational modification sites) and extracting biologically relevant regions.
+- **Task flexibility**: The model is not limited to PTM and BPS tasks. Users can adapt `PDeepPP` to other protein sequence-based tasks by customizing input data and task objectives.
+- **PTM mode**: Focuses on sequences centered around specific residues (S, T, Y) to analyze post-translational modification activity.
+- **BPS mode**: Analyzes overlapping or non-overlapping subsequences of a protein to extract biologically meaningful features.
 
 ## How to use