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| license: mit | |
| title: The Genomic Oracle | |
| sdk: static | |
| emoji: 🔥 | |
| colorFrom: indigo | |
| colorTo: purple | |
| short_description: UMGC Bioinformatics Capstone | |
| # 🧬 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 |