@article{e451aad4896e46c7abd49aee222cc87d,
title = "Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier",
author = "Utkarsh Tripathi and Liron Mizrahi and Martin Alda and Gregory Falkovich and Shani Stern",
note = "Funding Information: This material is based upon work supported by the Zuckerman STEM Leadership Program, ISF grant 1994/21, and ISF grant 3252/21 for Shani Stern and Simons foundation (662962), EU Horizon 2020 (83937, 873028) and BSF (2018033, 2020765) for Gregory Falkovich. The clinical part of this study was supported by grants from Genome Atlantic/ Reserach Nova Scotia (RPPP) and from ERA PerMed (PLOT‐BD) to Martin Alda. Funding Information: Zuckerman STEM Leadership Program for Shani Stern, ISF grant 1994/21, ISF grant 3252/21 for Shani Stern, Simons foundation (662962), EU Horizon 2020 (83937, 873028) and BSF (2018033, 2020765) for Gregory Falkovich. Publisher Copyright: {\textcopyright} 2022 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.",
year = "2023",
month = mar,
doi = "10.1111/bdi.13282",
language = "English",
volume = "25",
pages = "110--127",
journal = "Bipolar Disorders",
issn = "1398-5647",
publisher = "Blackwell Munksgaard",
number = "2",
}
