Interpretable and integrative deep learning for discovering brain-behaviour associations.

ABSTRACT: Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels often fail to capture the complexity and variability of these conditions. Recent research underlines the importance of considering multiple dimensions that span across different psychiatric syndromes. These developments have led to more comprehensive approaches to studying psychiatric conditions that incorporate diverse data sources such as imaging, genetics, and symptom reports. Multi-view unsupervised learning frameworks, particularly deep learning models, present promising solutions for integrating and analysing complex datasets. Such models co

SUBMITTER: Ambroise C 

PROVIDER: S-EPMC11742053 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature