Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

Journal: BMC medical research methodology
Published Date: Aug 2, 2024

Abstract

BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges.

Authors

  • Jack Kelly
    Open Climate Fix, London, United Kingdom.
  • Xiaoguang Xu
    Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
  • James M Eales
    Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
  • Bernard Keavney
    Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, M13 9PL, UK.
  • Carlo Berzuini
    Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
  • Maciej Tomaszewski
    Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK.
  • Hui Guo
    Health Sciences and Innovation, Surrey Memorial Hospital, Fraser Health Authority, Surrey, BC, Canada.

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