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Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using an SVM classifier

Utkarsh Tripathi, Liron Mizrahi, Martin Alda, Gregory Falkovich, Shani Stern
doi: https://doi.org/10.1101/2022.04.04.486856

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doi 
https://doi.org/10.1101/2022.04.04.486856
History 
  • April 5, 2022.
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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Utkarsh Tripathi1,
  2. Liron Mizrahi1,
  3. Martin Alda2,
  4. Gregory Falkovich3 and
  5. Shani Stern1,*
  1. 1Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel
  2. 2Department of Psychiatry, Dalhousie University, 5909 Veterans’ Memorial Lane, Halifax, NS B3H 2E2, Canada
  3. 3Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, ISRAEL
  1. ↵*corresponding author sstern{at}univ.haifa.ac.il
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Posted April 05, 2022.
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Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using an SVM classifier
Utkarsh Tripathi, Liron Mizrahi, Martin Alda, Gregory Falkovich, Shani Stern
bioRxiv 2022.04.04.486856; doi: https://doi.org/10.1101/2022.04.04.486856
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