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Predictix ™ - a user-friendly procedure that analyses genomic, clinical and demographic data to generate a personalized report on the efficacy of antidepressants and their side effects

Deliverables

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Publications

Impact of a Personalized Antidepressant Prescription Using Genetics, Socio-Demographic and Clinical Data in Major Depressive Disorder Patients: A Clinical Pilot Study: This paper was are the results of the initial clinical pilot with APHP and was published in Pharmacology and Pharmacogenetics

Author(s): Bertrand Saudreau, Amit Spinrad, Redwan Maatoug, Nourredine Haddadi, Raphaël Saffroy, Antoinette Lemoinee, Roy Schurr, Sne Darki-Morag, Roni Zoller, Dekel Taliaz, Bruno Millet
Published in: Pharmacology and Pharmacogenomics, 00000000, 2022, Page(s) 10, ISSN 0000-0000
Publisher: Pharmacology and Pharmacogenomics
DOI: 10.31488/jpp.105

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Author(s): Dekel Taliaz, Amit Spinrad, Ran Barzilay, Zohar Barnett-Itzhaki, Dana Averbuch, Omri Teltsh, Roy Schurr, Sne Darki-Morag, Bernard Lerer
Published in: Translational Psychiatry, 11/1, 2021, Page(s) 9, ISSN 2158-3188
Publisher: Nature Publishing Group
DOI: 10.1038/s41398-021-01488-3

Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path

Author(s): Dekel Taliaz and Prof. Alessandro Serretti
Published in: CNS & Neurological Disorders - Drug Targets, 18715273, 2022, Page(s) 8, ISSN 1871-5273
Publisher: Bentham Science Publishers
DOI: 10.2174/1871527321666220628103843

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Author(s): Dekel Taliaz, Daniel Souery
Published in: Journal of Clinical Medicine, 10/14, 2021, Page(s) 3109, ISSN 2077-0383
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/jcm10143109