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Identifying complex phenotypes from high-dimensional biological data ischallenging due to the intricate interdependencies among differentphysiological indicators. Traditional approaches often focus on detectingoutliers in single variables, overlooking the broader network of interactionsthat contribute to phenotype emergence. Here, we introduce ODBAE (OutlierDetection using Balanced Autoencoders), a machine learning method designed touncover both subtle and extreme outliers by capturing latent relationshipsamong multiple physiological parameters. ODBAE's revised loss function enhancesits ability to detect two key types of outliers: influential points (IP), whichdisrupt latent correlations between dimensions, and high leverage points (HLP),which deviate from the norm but go undetected by traditional autoencoder-basedmethods. Using data from the International Mouse Phenotyping Consortium (IMPC),we show that ODBAE can identify knockout mice with complex, multi-indicatorphenotypes - normal in individual traits, but abnormal when consideredtogether. In addition, this method reveals novel metabolism-related genes anduncovers coordinated abnormalities across metabolic indicators. Our resultshighlight the utility of ODBAE in detecting joint abnormalities and advancingour understanding of homeostatic perturbations in biological systems.

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