ArticleTranslational psychiatry2019
Depression and suicide risk prediction models using blood-derived multi-omics data.
Article in Translational psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Recent developments in omics studies and artificial intelligence in depression and suicide.Translational psychiatry · 2025Pooled it
- Integrating Forensic Autopsies with Proteomic Profiling for Suicide Risk Assessment: A Comprehensive Review of Literature.Current neuropharmacology · 2025Pooled it
- Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches.Translational psychiatry · 2024Pooled it
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Multi-omics approaches to major psychiatric disorders.Neuropsychiatrie : Klinik, Diagnostik, Therapie und Rehabilitation : Organ der Gesellschaft Osterreichischer Nervenarzte und Psychiater · 2026Review
- Identifying past-year self-reported suicidality in outpatients with somatic symptom disorder using an interpretable machine-learning model: a multicenter study with an online calculator.BMC psychiatry · 2026Observational
- Establishment and Validation of Machine Learning Model for Predicting Suicide Risk in Patients with Major Depressive Disorder.Neuropsychiatric disease and treatment · 2026Article
- Combined social determinants of health contributed to adverse health outcomes among depression: evidence from two national cohorts.Epidemiology and psychiatric sciences · 2025Article
- Dynamic and reversible transcriptomic age shifts induced by COVID-19 in Korean whole blood.Aging · 2025Article
- National Trends and Directions for Suicide Prevention Research in South Korea: A Narrative Review.Psychiatry investigation · 2025Article
- A Novel Framework for the Design of Minimized Epigenetic Clocks Using the Analysis of DNA Methylation Heterogeneity.International journal of molecular sciences · 2025Article
- Epigenetics and suicidal behavior in adolescents: a critical review.Epigenomics · 2025Review
- Alzheimer's disease may develop from changes in the immune system, cell cycle, and protein processing following alterations in ribosome function.Scientific reports · 2025Article
- Using Machine Learning and the HAMD-24 Scale to Predict Suicide Ideation in Depressed Patients.Psychology research and behavior management · 2025Article
- The researcher's guide to selecting biomarkers in mental health studies.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.JMIR nursing · 2024Article
- Epigenome-wide Association Study for Tic Disorders in Children: A Preliminary Study in Korean Population.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Article
- Age Prediction Using DNA Methylation Heterogeneity Metrics.International journal of molecular sciences · 2024Article
- Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution ofDepression and anxiety · 2024Observational
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R
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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.