QLattice

QLattice: Symbolic Regression for Interpretable Biomarker Discovery

QLattice applies symbolic regression to omics data to generate parsimonious, predictive, and interpretable mathematical models that identify relevant biomarker signatures and predict disease outcomes.

Key Features:

• Symbolic Regression-Based Algorithm: Searches for mathematical expressions that best describe relationships within omics datasets, producing compact and predictive models.
• Feature Selection: Selects maximally relevant and minimally redundant features to construct robust, simplified biomarker signatures.
• Predictive Performance and Interpretability: Balances high predictive accuracy with transparent model structures that reveal potential disease mechanisms.

Scientific Applications:

• Clinical Omics Analysis: Identifies biomarker signatures for disease outcome prediction, patient stratification, and clinical decision-making.

Methodology:

QLattice performs symbolic regression by systematically exploring mathematical model structures and optimizing them according to predictive performance and parsimony. The approach emphasizes relevance-driven feature selection and redundancy reduction to enhance interpretability and biological insight in clinical omics datasets.