Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Autor: Yang Z; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA., Wen J; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA., Abdulkadir A; Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland., Cui Y; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Erus G; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Mamourian E; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Melhem R; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Srinivasan D; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Govindarajan ST; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Chen J; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Habes M; Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA., Masters CL; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia., Maruff P; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia., Fripp J; CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia., Ferrucci L; Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA., Albert MS; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Johnson SC; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA., Morris JC; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA., LaMontagne P; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA., Marcus DS; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA., Benzinger TLS; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA., Wolk DA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA., Shen L; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA., Bao J; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA., Resnick SM; Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA., Shou H; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA., Nasrallah IM; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Davatzikos C; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Christos.Davatzikos@pennmedicine.upenn.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Jan 08; Vol. 15 (1), pp. 354. Date of Electronic Publication: 2024 Jan 08.
DOI: 10.1038/s41467-023-44271-2
Abstrakt: Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
(© 2024. The Author(s).)
Databáze: MEDLINE
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