ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets.

ABSTRACT: Identifying complex phenotypes from high-dimensional biological data is challenging due to the intricate interdependencies among different physiological indicators. Traditional approaches often focus on detecting outliers in single variables, overlooking the broader network of interactions that contribute to phenotype emergence. Here, we introduce ODBAE (Outlier Detection using Balanced Autoencoders), a machine learning method designed to uncover both subtle and extreme outliers by capturing latent relationships among multiple physiological parameters. ODBAE's revised loss function enhances its ability to detect two key types of outliers: influential points (IP), which disrupt latent correlations between dimensions, and high leverage points (HLP), which deviate from the norm but go undetec

SUBMITTER: Shen Y 

PROVIDER: S-EPMC12491551 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature