Cargando…
Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication
We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied...
Autores principales: | Athreya, Arjun P., Neavin, Drew, Carrillo‐Roa, Tania, Skime, Michelle, Biernacka, Joanna, Frye, Mark A., Rush, A. John, Wang, Liewei, Binder, Elisabeth B., Iyer, Ravishankar K., Weinshilboum, Richard M., Bobo, William V. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739122/ https://www.ncbi.nlm.nih.gov/pubmed/31012492 http://dx.doi.org/10.1002/cpt.1482 |
Ejemplares similares
-
Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings
por: Athreya, Arjun P., et al.
Publicado: (2021) -
Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication
por: Joyce, Jeremiah B., et al.
Publicado: (2021) -
Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder
por: Grant, Caroline W., et al.
Publicado: (2022) -
ERICH3: vesicular association and antidepressant treatment response
por: Liu, Duan, et al.
Publicado: (2020) -
Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics
por: Liu, Duan, et al.
Publicado: (2018)