Update README.md

README.md

@@ -1,16 +1,25 @@
-# GvEM - Genomic Variant Embedding Model
-
-**HierarchicalVCF** is a PyTorch-based deep learning model designed to embed and model genomic mutation data from VCF (Variant Call Format) files using a biologically-informed hierarchy:
+---
+'[object Object]': null
+license: apache-2.0
+language:
+- en
+pipeline_tag: token-classification
+tags:
+- RepresentationLearning
+- Genomics
+- Variant
+- Classiciation
+- Mutations
+- Embedding
+- VariantClassificaion
+---
+
+# Model - GvEM (Genomic Variant Embedding Model)
+
+**GvEM** is a PyTorch-based deep learning model designed to embed and model genomic mutation data from VCF (Variant Call Format) files using a biologically-informed hierarchy:
 **Pathway → Chromosome → Gene → Mutations**
 
-This model enables:
-
-* Pathway-aware embedding of genomic variants.
-* Learning from multi-level hierarchies of mutation context.
-* Generalization across diseases and datasets via scalable, modular design.
-
 ---
-
 ## Hierarchy of input data
 
    example_data = {
@@ -30,38 +39,34 @@ This model enables:
     }
 
 ---
-
 ## Features
 
-* ✅ **VCF Parser**: Converts standard VCF files into a hierarchical JSON-like structure.
-* ✅ **MutationEmbedder**: Learns embeddings for categorical mutation features (scalable).
-* ✅ **GeneEncoder**: Processes lists of mutations using Transformer and heirarchical attention to get gene-level representations.
-* ✅ **ChromosomeEncoder**: Aggregates gene encodings.
-* ✅ **PathwayEncoder**: Aggregates chromosome encodings to yield final sample representation.
-* ✅ **Scalable**: Easily extensible to new fields or biological groupings.
-* ✅ **HuggingFace Compatible**: Designed for sharing and experimentation on the 🤗 Hub.
-
+* **VCF Parser**: Converts standard VCF files into a hierarchical JSON-like structure.
+* **MutationEmbedder**: Learns embeddings for categorical mutation features (scalable).
+* **GeneEncoder**: Processes lists of mutations using Transformer and heirarchical attention to get gene-level representations.
+* **ChromosomeEncoder**: Aggregates gene encodings.
+* **PathwayEncoder**: Aggregates chromosome encodings to yield final sample representation.
+* **Scalable**: Easily extensible to new fields or biological groupings.
+* **HuggingFace Compatible**: Designed for sharing and experimentation on the 🤗 Hub.
 ---
+## Uses
 
-## Use Cases
-
-* Variant-based disease classification (e.g., cancer, rare diseases, ASD)
-* Multi-omics fusion models (tabular + image + VCF)
-* Knowledge-driven mutation impact modeling
+# Direct Use :
+* Obtain sample level embeddings
+* Mutation pattern learning
 * Transfer learning across genomic datasets
 
+# Downstream Use :
+* Variant-based disease prediction (e.g., cancer, rare diseases, ASD)
+* Multi-omics fusion models (tabular + image + VCF)
+* Cohort level mutation analysis
+* Fine-tuning for prognosis, drug response prediction, or variant effect interpretation.
+
+# Limitations
+* Use in clinical decision-making without expert oversight.
+* Input variants must already be annotated.
+* Application to non-human genomes, unless explicitly fine-tuned for those organisms.
+* High-resolution functional variant prediction - FUTURE DEVELOPMENT TO BE MADE
 ---
 
-## 📂 Folder Structure
-
-```
-HierarchicalVCF/
-├── vcf_parser.py        # Parses VCF files into hierarchical format
-├── model.py             # Model components (MutationEmbedder, encoders, classification head)
-├── tokenizer.py         # Vocabulary tokenizer for categorical fields
-├── VCFDataset.py        # Torch dataset compiler
-├── train.py             # Sample training pipeline
-├── README.md            # This file
-```
-
 ## MODEL STILL UNDER DEVELOPMENT