APA (7th ed.) Citation

Tripathi, U., Mizrahi, L., Alda, M., Falkovich, G., & Stern, S. (2023). Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier. Bipolar disorders, 25(2), 110-127. https://doi.org/10.1111/bdi.13282

Chicago Style (17th ed.) Citation

Tripathi, Utkarsh, Liron Mizrahi, Martin Alda, Gregory Falkovich, and Shani Stern. "Information Theory Characteristics Improve the Prediction of Lithium Response in Bipolar Disorder Patients Using a Support Vector Machine Classifier." Bipolar Disorders 25, no. 2 (2023): 110-127. https://doi.org/10.1111/bdi.13282.

MLA (9th ed.) Citation

Tripathi, Utkarsh, et al. "Information Theory Characteristics Improve the Prediction of Lithium Response in Bipolar Disorder Patients Using a Support Vector Machine Classifier." Bipolar Disorders, vol. 25, no. 2, 2023, pp. 110-127, https://doi.org/10.1111/bdi.13282.