Update README.md

README.md

@@ -19,10 +19,10 @@ As genomic datasets grow exponentially, the need for rapid, automated sequence a
 ## 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 baseline machine learning classifier that rapidly screens raw k-mer vectors to identify protein-coding potential.
+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 fine-tuned transformer model that analyzes spatial attention tensors to identify regulatory promoter regions.
+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.