ReviewClinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology2021
Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments.
Review in Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Pattern Recognition Algorithms in Pharmacogenomics and Drug Repurposing-Case Study: Ribavirin and Lopinavir.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures.Journal of clinical medicine · 2025Review
- Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Validating the accuracy of mathematical model-based pharmacogenomics dose prediction with real-world data.European journal of clinical pharmacology · 2025Article
- Machine Learning Algorithms for the Prediction of Locomotor Activity by an Infrared Motion Detector on the Sleep-wake States in Mice.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2023Article
- Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin.International journal of clinical pharmacy · 2023Article
- Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.International journal of molecular sciences · 2021Review
- Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach.Journal of personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
Indexed as
Identifiers
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.