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EXPLANA: A user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies.

Jennifer FouquierMaggie A StanislawskiJohn B O'ConnorAshley W ScaddenCatherine A Lozupone
Published in: bioRxiv : the preprint server for biology (2024)
To address these challenges, we developed a feature selection workflow for cross-sectional and LMS that supports numerical and categorical data called EXPLANA (EXPLoratory ANAlysis). Machine-learning methods were combined with different types of change calculations and downstream interpretation methods to identify statistically meaningful variables and explain their relationship to outcomes. EXPLANA generates an interactive report that textually and graphically summarizes methods and results. EXPLANA had good performance on simulated data, with an average area under the curve (AUC) of 0.91 (range: 0.79-1.0, SD = 0.05), outperformed an existing tool (AUC: 0.95 vs. 0.56), and identified novel order-dependent categorical feature changes. EXPLANA is broadly applicable and simplifies analytics for identifying features related to outcomes of interest.
Keyphrases
  • machine learning
  • cross sectional
  • big data
  • electronic health record
  • deep learning
  • artificial intelligence
  • molecular dynamics
  • metabolic syndrome
  • skeletal muscle
  • weight loss
  • drug induced
  • case control