Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging

Abstract excerpt

Sex classification using neuroimaging data has the potential to revolutionize personalized diagnostics by revealing subtle structural brain differences that underlie sex-specific disease risks. Despite the promise of machine learning, traditional methods often fall short in providing both high classification accuracy and interpretable, statistically validated feature importance scores for high-dimensional imaging...

Topics

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• Functional Brain Connectivity Studies
• Limbic System
• Machine Learning
• Major Depressive Disorder
• Mental Disorders
• Phenotype
• Sex Characteristics
• Young Adult

Identifiers and source

Literature Corpus work

38080bc7-694a-5b64-bd07-e8ac7837f48a

DOI

10.1101/2025.07.09.664026