README.md

---
title: Genomic Oracle v2
emoji: 🚀
colorFrom: indigo
colorTo: purple
sdk: gradio
app_file: app.py
tags:
- gradio
pinned: false
short_description: Classify Human Genomic Sequence Instantly
license: mit
sdk_version: 6.9.0
---
# 🧬 The Genomic Oracle 🧬 

**University of Maryland Global Campus (UMGC) | Bioinformatics Capstone Project**

Welcome to the official repository for **The Genomic Oracle**, a cascaded machine learning pipeline designed for high-precision DNA sequence classification and phenotypic prediction. 

## Our Mission
As genomic datasets grow exponentially, the need for rapid, automated sequence annotation is critical. The Genomic Oracle serves as an intelligent routing network, evaluating raw DNA sequences through a multi-stage gauntlet to classify their biological function, structural feature type, and associated phenotypic risks.

## The 4-Stage Cascading Architecture
Our platform utilizes a highly specialized, branching AI architecture hosted on Hugging Face ZeroGPU infrastructure:

1. **Level 1: The Gene Finder (Coding vs. Non-Coding):** A logistic regression machine learning classifier that rapidly screens raw k-mer vectors to identify protein-coding potential.
2. **Level 2: The Multi-Feature Classifier (LightGBM):** Sequences flagged as coding are passed through a LightGBM gradient boosting model to classify them into one of 7 highly specific structural features.
3. **Level 3: Phenotype Classification (Lean vs. Obese):** A custom ALiBi-configured BERT architecture evaluates specific coding regions to predict downstream phenotypic associations.
4. **Level 4: The Promoter Network (DNABERT-2):** Sequences flagged as non-coding are routed to a neural network transformer model that analyzes spatial attention tensors to identify regulatory promoter regions.

**Integration:** The pipeline concludes with an automated API routing to NCBI and Ensembl databases for real-world chromosomal coordinate mapping and validation.

## The Team
We are a team of graduate researchers specializing in data science, machine learning, and bioinformatics. 

Created by: Kadir Galindo, Duncan Hall, Rebecca Mellinger, George Paccione