Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier-Reference-Cited by-同舟云学术

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Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier

Published:2022-12-18 Issue:2 Volume:25 Page:110-127
ISSN:1398-5647
Container-title:Bipolar Disorders
language:en
Short-container-title:Bipolar Disorders

Author:

Tripathi Utkarsh¹^ORCID,Mizrahi Liron¹,Alda Martin²^ORCID,Falkovich Gregory³^ORCID,Stern Shani¹^ORCID

Affiliation:

1. Sagol Department of Neurobiology, Faculty of Natural Sciences University of Haifa Haifa Israel

2. Department of Psychiatry Dalhousie University Halifax Nova Scotia Canada

3. Department of Physics of Complex Systems Weizmann Institute of Science Rehovot Israel

Funder

Israel Science Foundation

Simons Foundation

Bonfils-Stanton Foundation

Publisher

Wiley

Subject

Biological Psychiatry,Psychiatry and Mental health

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/bdi.13282

Reference46 articles.

1. Bipolar disorders

2. The Long-term Natural History of the Weekly Symptomatic Status of Bipolar I Disorder

3. Suicidal Behaviour in Bipolar Disorder

4. Suicide as an outcome for mental disorders

5. Pharmacological Approaches for Treatment-resistant Bipolar Disorder

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1. Predicting Suicide Risk in Bipolar Disorder patients from Lymphoblastoid Cell Lines genetic signatures;2024-05-31

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3. Brain Extracellular Matrix implications in multiple neurological disorders are revealed through a meta-analysis of transcriptional changes;2024-05-19

4. The Impact of Electrophysiological Diversity on Pattern Completion in Lithium Nonresponsive Bipolar Disorder: A Computational Modelling Approach;2023-11-20

5. Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients;Molecular Psychiatry;2023-07-24

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