ArticleFrontiers in psychiatry2018
A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers.
Article in Frontiers in psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Structural imaging predictors of ketamine response in treatment-resistant depression: a machine learning approach.Translational psychiatry · 2026Article
- Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-Computational statistics & data analysis · 2026Article
- NELL-1: role and mechanisms in disease pathogenesis and development.Frontiers in cell and developmental biology · 2026Review
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients.Schizophrenia (Heidelberg, Germany) · 2025Article
- Pharmaco-Multiomics: A New Frontier in Precision Psychiatry.International journal of molecular sciences · 2025Review
- Schizophrenia risk gene ZNF536 modulates retinoic acid response and neuronal gene networks in SH-SY5Y cells.Frontiers in molecular neuroscience · 2025Article
- Exercise improves pulmonary fibrosis and neurological symptoms via S100A12 inhibition.Frontiers in immunology · 2025Article
- Utilising AI technique to identify depression risk among doctoral students.Scientific reports · 2024Article
- RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.International journal of molecular sciences · 2024Article
- Review
- Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment.World journal of psychiatry · 2024Review
- Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Areca Thirteen Pill Improves Depression in Rat by Modulation of the Chemokine/Chemokine Receptor Axis.Molecular neurobiology · 2024Article
- PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service.Applied clinical informatics · 2024Article
- Impairments of cerebellar structure and function in a zebrafish KO of neuropsychiatric risk gene znf536.Translational psychiatry · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract.Microorganisms · 2024Review
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including
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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.