The international journal of neuropsychopharmacology
Nov 26, 2020
BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of...
Active compounds and corresponding targets of the traditional Chinese herb, were obtained from systems pharmacological database and placed into ClueGO for gene ontology analysis. The targets of depression were obtained from the Online Mendelian Inhe...
Journal of consulting and clinical psychology
Jan 1, 2020
OBJECTIVE: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of st...
BACKGROUND: Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversel...
OBJECTIVE: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome.
IMPORTANCE: Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified.
Combinatorial chemistry & high throughput screening
Jan 1, 2015
The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biologic...
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