# Model Card for Bio-Foundation Model

## Model Details

**Model Name:** Bio-Foundation Model v1.0  
**Model Version:** 1.0  
**Model Description:** A large pre-trained model for analyzing DNA and RNA sequences. The model is designed to perform tasks such as sequence alignment, mutation detection, and gene prediction.

## Model Developers

**Developed By:** BioAI Research Team  
**Contact Information:** bioai@researchlab.com  
**License:** Apache 2.0

## Model Type

**Architecture:** Transformer-based  
**Domain:** Genomics, Bioinformatics  
**Input Data:** Single-cell transcriptomics data

## Model Purpose

**Intended Use:**  
- Research in genomics and bioinformatics
- Clinical diagnostics support
- Educational purposes

**Out-of-Scope Use Cases:**  
- Direct clinical decision making without human oversight
- Any applications outside the scope of DNA/RNA analysis

## Training Data

**Data Sources:**  
- Publicly available DNA/RNA sequence databases (e.g., NCBI GenBank, ENCODE)
- Synthetic data generated to balance dataset

**Data Volume:**  
- 1TB of raw DNA/RNA sequence data

**Preprocessing:**  
- Standardized to remove low-quality sequences
- Balanced to include diverse species and sequence types

## Model Performance

**Evaluation Metrics:**  
- Accuracy, Precision, Recall, F1-Score for various tasks (e.g., mutation detection, gene prediction)
- Specific benchmarks include sequence alignment accuracy and variant calling accuracy

**Performance Benchmarks:**  
- Mutation Detection: Precision 0.95, Recall 0.93
- Gene Prediction: Precision 0.90, Recall 0.88

**Testing Data:**  
- Held-out subset of the training dataset
- Additional external validation datasets

## Ethical Considerations

**Bias and Fairness:**  
- Ensured diverse representation of species and sequence types in the training data
- Ongoing evaluation for any biases, particularly those that may impact underrepresented species

**Privacy:**  
- All training data sourced from public databases with appropriate usage permissions
- No use of private or sensitive genetic data without explicit consent

**Mitigations:**  
- Regular audits of model outputs to detect and correct biases
- Collaboration with ethicists and domain experts to ensure responsible use

## Model Limitations

**Known Limitations:**  
- May not generalize well to newly discovered species or rare sequence variants
- Performance may vary across different sequencing technologies

**Future Improvements:**  
- Continuous integration of new data sources
- Enhancements in model architecture to better handle rare variants

## How to Use

**Input Format:**  
- FASTA format for DNA/RNA sequences

**Output Format:**  
- JSON format with predicted sequence alignments, mutations, and gene locations

**Example Usage:**
```python
from bio_foundation_model import BioFoundationModel

# Initialize model
model = BioFoundationModel()

# Load DNA sequence
sequence = ">seq1\nATGCGTACGTAGCTAGCTAGCTA"

# Predict
result = model.predict(sequence)

print(result)
```


## Developers

## Contact

## Citation