Description
AI-Driven Shortlisting of Clinico-Multiomic Features
From Tens of Hundreds of Features to the Few That Matter Most
After validating biological relevance, Bainom applies advanced AI/ML algorithms and evidence-based scoring to intelligently prioritize features across omics and clinical layers. This process includes:
- Minimal Feature Selection via Machine Learning: We train and evaluate multiple models—linear and non-linear regression, random forest, and advanced algorithms — to identify the smallest set of features that best explain the condition. Using iterative model optimization and permutation testing, we ensure robustness and predictive power.
- Feature Importance & Explainability: AI models provide importance metrics (e.g., SHAP values, Gini importance) to rank features based on their contribution to predictive accuracy, ensuring transparency and interpretability.
- Network Topology Analysis: Combining graph-theoretic measures (centrality, connectivity) with ML-derived importance scores to identify hub features critical for biological networks.
- Literature Attention Scoring via NLP: Using natural language processing to mine top publications for each feature, scoring its documented involvement in the condition of interest.
By integrating expression levels, biomarker status, ML-driven minimal sets, network topology, and NLP-based attention scores, Bainom delivers a shortlist of features that are statistically sound, biologically meaningful, and clinically relevant—ready for downstream interpretation or predictive modeling.
