Optimizing Model Performance and Interpretability: Application to Biological Data Classification.

ABSTRACT: This study introduces a novel framework that simultaneously addresses the challenges of performance accuracy and result interpretability in transcriptomic-data-based classification. Background/objectives: In biological data classification, it is challenging to achieve both high performance accuracy and interpretability at the same time. This study presents a framework to address both challenges in transcriptomic-data-based classification. The goal is to select features, models, and a meta-voting classifier that optimizes both classification performance and interpretability. Methods: The framework consists of a four-step feature selection process: (1) the identification of metabolic pathways whose enzyme-gene expressions discriminate samples with different labels, aiding inter

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11942234 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature