MLOmics
LiveOverview
MLOmics is a reproducible machine learning pipeline for cancer molecular subtype classification using multi-omics integration. It combines gene expression, microRNA, DNA methylation, and copy number variation data from 931 TCGA patients across breast cancer (BRCA) and colon cancer (COAD) cohorts.
The project evaluates tree-based baselines and deep learning fusion models, including a pathway-aware fusion architecture that injects KEGG biological pathway knowledge into the mRNA encoder. It also includes explainability workflows and a Streamlit demo for prediction, model comparison, and lab-format data conversion.
Architecture
Challenges & Solutions
Multi-omics data has extremely high dimensionality with limited patient counts, making overfitting a major risk.
Applied feature selection, 5-fold stratified cross-validation, and modality-specific encoders to control overfitting across all model families.
Black-box predictions are insufficient for biomedical ML, where interpretability directly affects clinical trust.
Added SHAP, Integrated Gradients, and enrichment visualizations to make every prediction traceable to input features and biological pathways.
Research code often becomes notebook-only and is hard to demo or hand off to others.
Built a Streamlit application exposing prediction, model comparison, explainability, and lab-format data conversion workflows.
Key Results
BRCA Macro F1
0.808
±0.050 with Intermediate Fusion
COAD AUC
0.954
±0.025 with Pathway-Aware Fusion
TCGA Patients
931
Across BRCA and COAD cohorts
Omics Features
15,000+
Per cohort after preprocessing
Features
- Multi-omics preprocessing for mRNA, miRNA, methylation, and CNV data
- XGBoost and Random Forest early-fusion baseline models
- PyTorch IntermediateFusionModel and PathwayAwareFusionModel
- KEGG pathway-informed neural architecture for mRNA encoding
- SHAP and Integrated Gradients explainability workflows
- 5-fold stratified cross-validation across 40 model checkpoints
- Streamlit demo for prediction, model comparison, and data conversion
- Lab Data Converter for hospital-format per-modality CSV files
