AIMC Topic: Antidepressive Agents

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Antidepressant pathways of the Chinese herb through genetic ontology analysis.

Journal of integrative neuroscience Jun 30, 2020
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...
Humans Antidepressive Agents Phytotherapy Gene Ontology Pharmacogenetics Drugs, Chinese Herbal
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Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

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...
Humans Machine Learning Precision Medicine Depressive Disorder Hospitals, Psychiatric Antidepressive Agents Cognitive Behavioral Therapy Adult Male Adolescent Prognosis Reproducibility of Results Middle Aged Treatment Outcome Aged Female Young Adult
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Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: can computational intelligence be used to augment clinical assessments?

Pharmacogenomics Sep 1, 2019
Biomarkers Pharmacogenetics Polymorphism, Single Nucleotide Machine Learning Algorithms Antidepressive Agents Depressive Disorder Humans Artificial Intelligence
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Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.

Epidemiology (Cambridge, Mass.) Jul 1, 2019
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...
Practice Patterns, Physicians' Antidepressive Agents Off-Label Use Logistic Models Data Interpretation, Statistical Primary Health Care Canada Machine Learning Algorithms Humans
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Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

The Journal of clinical psychiatry Jan 1, 2018
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.
Humans Machine Learning Female Adult Forecasting Antidepressive Agents Depressive Disorder, Treatment-Resistant Male Depressive Disorder, Major Treatment Outcome Middle Aged Models, Statistical
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Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

JAMA psychiatry Jun 1, 2016
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.
Biomarkers Antidepressive Agents Humans Female Gyrus Cinguli Depressive Disorder, Major Male Adult Machine Learning Predictive Value of Tests Electroconvulsive Therapy Treatment Outcome Combined Modality Therapy Prospective Studies Middle Aged Magnetic Resonance Imaging
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A Study of Applications of Machine Learning Based Classification Methods for Virtual Screening of Lead Molecules.

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...
Hypoglycemic Agents Anticonvulsants Drug Delivery Systems Drug Design Machine Learning Arrhythmias, Cardiac Antidepressive Agents Anti-Bacterial Agents
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