Geonomic commited on
Commit
da0a7d0
·
verified ·
1 Parent(s): c116b66

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +24 -1
README.md CHANGED
@@ -11,4 +11,27 @@ pinned: false
11
  short_description: Classify Human Genomic Sequence Instantly
12
  license: mit
13
  sdk_version: 6.9.0
14
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
  short_description: Classify Human Genomic Sequence Instantly
12
  license: mit
13
  sdk_version: 6.9.0
14
+ ---
15
+ # 🧬 The Genomic Oracle 🧬
16
+
17
+ **University of Maryland Global Campus (UMGC) | Bioinformatics Capstone Project**
18
+
19
+ Welcome to the official repository for **The Genomic Oracle**, a cascaded machine learning pipeline designed for high-precision DNA sequence classification and phenotypic prediction.
20
+
21
+ ## Our Mission
22
+ 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.
23
+
24
+ ## The 4-Stage Cascading Architecture
25
+ Our platform utilizes a highly specialized, branching AI architecture hosted on Hugging Face ZeroGPU infrastructure:
26
+
27
+ 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.
28
+ 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.
29
+ 3. **Level 3: Phenotype Classification (Lean vs. Obese):** A custom ALiBi-configured BERT architecture evaluates specific coding regions to predict downstream phenotypic associations.
30
+ 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.
31
+
32
+ **Integration:** The pipeline concludes with an automated API routing to NCBI and Ensembl databases for real-world chromosomal coordinate mapping and validation.
33
+
34
+ ## The Team
35
+ We are a team of graduate researchers specializing in data science, machine learning, and bioinformatics.
36
+
37
+ Created by: Kadir Galindo, Duncan Hall, Rebecca Mellinger, George Paccione