Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA Article

Ruiz-Perez, Daniel, Gimon, Isabella, Sazal, Musfiqur et al. (2024). Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA . MSYSTEMS, 9(10), 10.1128/msystems.01303-23

Open Access

cited authors

  • Ruiz-Perez, Daniel; Gimon, Isabella; Sazal, Musfiqur; Mathee, Kalai; Narasimhan, Giri

sustainable development goals

authors

publication date

  • October 22, 2024

published in

keywords

  • ACETIC-ACID BACTERIA
  • DIVERSITY
  • ECOLOGY
  • LIFE
  • Life Sciences & Biomedicine
  • Microbiology
  • QUALITY
  • SEQUENCE ALIGNMENT
  • SOURDOUGH MICROBIOTA
  • STRAINS
  • SYSTEMS
  • Science & Technology
  • causal inference
  • de-confounding
  • longitudinal microbiome analysis
  • multi-omic integration
  • unfolding

Digital Object Identifier (DOI)

publisher

  • AMER SOC MICROBIOLOGY

volume

  • 9

issue

  • 10