Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS.

Journal: Frontiers in computational neuroscience
Published Date: Jan 4, 2024

Abstract

INTRODUCTION: Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Authors

  • Joseph Geraci
    NetraMark Corp, Toronto, ON, Canada.
  • Ravi Bhargava
    Department of Biomedical and Molecular Science, Queens University, Kingston, ON, Canada.
  • Bessi Qorri
    NetraMark Corp, Toronto, ON, Canada.
  • Paul Leonchyk
    NetraMark Corp, Toronto, ON, Canada.
  • Douglas Cook
    NetraMark Corp, Toronto, ON, Canada.
  • Moses Cook
    Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
  • Fanny Sie
    Science and Research, Roche Integrated Informatics, F. Hoffmann La-Roche, Toronto, ON, Canada.
  • Luca Pani
    NetraMark Corp, Toronto, ON, Canada.

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