README.md

---
'[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**

---
## Hierarchy of input data

   example_data = {
        'sample1': {
            'pathway1': {
                'chr1': {
                    'gene1': [
                        {
                            'impact': 'HIGH',
                            'reference': 'A',
                            'alternate': 'T'
                        }
                    ]
                }
            }
        }
    }

---
## 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.
---
## Uses

# 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
---

## MODEL STILL UNDER DEVELOPMENT