ArticleScience advances2022
Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- Deep learning-based stratification of Schizophrenia Spectrum Disorder from real-world data reveals distinct profiles of common and rare variant genetic signal.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Modeling psychopathology in high-dimensional vector space using the high-dimensional symptom space (HDSS) model can operationalize precision psychiatry in US adolescents.Scientific reports · 2025Article
- Rapid prediction of acute thrombosis via nanoengineered immunosensors with unsupervised clustering for multiple circulating biomarkers.Science advances · 2024Article
- Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.Journal of integrative bioinformatics · 2024Article
- Development and Validation of a Machine Learning-Based Model of Mortality Risk in First-Episode Psychosis.JAMA network open · 2024Article
- Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.Nature biotechnology · 2023Article
- Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data.JAMA psychiatry · 2023Article
- Towards precision psychiatry utilizing large-scale multimodal data paving the way for improved prevention and treatment of mental disorders.Neuroscience applied · 2023Article
- Whole Person Modeling: a transdisciplinary approach to mental health research.Discover mental health · 2023Review
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we performed unsupervised patient stratification of 19,636 patients with depression [major depressive disorder (MDD)] and/or schizophrenia (SCZ) and 22,467 population controls from the iPSYCH2012 case cohort. We integrated data of disorder severity, history of mental disorders and disease comorbidities, genetics, and medical birth data. From this, we stratified the individuals in six and seven unique clusters for MDD and SCZ, respectively. When censoring data until diagnosis, we could predict MDD clusters with areas under the curve (AUCs) of 0.54 to 0.80 and SCZ clusters with AUCs of 0.71 to 0.86. Overall cases and controls could be predicted with an AUC of 0.81, illustrating the utility of data-driven subgrouping in psychiatry.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.