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README.md
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short_description: Classify Human Genomic Sequence Instantly
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license: mit
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sdk_version: 6.9.0
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short_description: Classify Human Genomic Sequence Instantly
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license: mit
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sdk_version: 6.9.0
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---
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# 🧬 The Genomic Oracle 🧬
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**University of Maryland Global Campus (UMGC) | Bioinformatics Capstone Project**
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Welcome to the official repository for **The Genomic Oracle**, a cascaded machine learning pipeline designed for high-precision DNA sequence classification and phenotypic prediction.
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## Our Mission
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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.
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## The 4-Stage Cascading Architecture
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Our platform utilizes a highly specialized, branching AI architecture hosted on Hugging Face ZeroGPU infrastructure:
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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.
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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.
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3. **Level 3: Phenotype Classification (Lean vs. Obese):** A custom ALiBi-configured BERT architecture evaluates specific coding regions to predict downstream phenotypic associations.
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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.
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**Integration:** The pipeline concludes with an automated API routing to NCBI and Ensembl databases for real-world chromosomal coordinate mapping and validation.
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## The Team
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We are a team of graduate researchers specializing in data science, machine learning, and bioinformatics.
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Created by: Kadir Galindo, Duncan Hall, Rebecca Mellinger, George Paccione
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