ml-integration-patterns

What problem does it solve?

The Skill provides structured patterns to integrate machine learning into the rRNA-Phylo project, enabling scalable ML-driven workflows for sequence classification, tree consensus, and generative tree synthesis.

Core Features & Use Cases

• rRNA Sequence Classification: Supervised learning to identify rRNA types from DNA/RNA sequences using feature engineering (e.g., k-mer frequencies) and classical or deep learning models.
• Multi-Tree Consensus: Ensemble approaches to combine trees from different methods into a robust consensus.
• Generative Tree Synthesis: Experimental Graph Neural Networks and Transformers-based approaches to generate phylogenetic trees from multiple inputs.
• Model Serving & Versioning: Patterns for serving models via APIs and versioning with performance metrics.

Quick Start

Train a small k-mer based classifier using the provided feature extractor and a RandomForest model on a labeled rRNA dataset to see results quickly.