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

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 cont...

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Bibliographic Details
Published in:Communications biology Vol. 8; no. 1; pp. 1415 - 19
Main Authors: Shen, Yafei, Zhang, Tao, Liu, Zhiwei, Kostelidou, Kalliopi, Xu, Ying, Yang, Ling
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 02.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
631/1647/48
631/1647/794
Animals
Biomedical and Life Sciences
Datasets
Disease
Genes
Genetic engineering
Homeostasis
Life Sciences
Machine Learning
Metabolism
Mice
Mice, Knockout
Performance evaluation
Phenotype
Phenotypes
Phenotyping
Physiology
ISSN:2399-3642, 2399-3642
Online Access:Get full text
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Summary: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 undetected by traditional autoencoder-based methods. Using data from the International Mouse Phenotyping Consortium (IMPC), we show that ODBAE can identify knockout mice with complex, multi-indicator phenotypes—normal in individual traits, but abnormal when considered together. In addition, this method reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators. Our results highlight the utility of ODBAE in detecting joint abnormalities and advancing our understanding of homeostatic perturbations in biological systems. ODBAE offers a powerful approach for detecting complex anomalies and characterizing unknown phenotypes within biological systems.
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ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-025-08817-y