Registered Data

[02068] Variable selection aided by correlation networks

  • Session Time & Room : 5D (Aug.25, 15:30-17:10) @D515
  • Type : Contributed Talk
  • Abstract : Variable selection is important because it can provide improved quality of results, faster times of computation and more explainable models. We present recent work in which we use data from over 100 000 cells to find a selection of morphokinetic variables guided by nonlinear correlation networks, able to capture behavioral landscapes of inflammation. Our mathematical modeling, based on logistic and decision tree models, allowed us to identify the most important variables for immune cell prediction.
  • Classification : 92-08, 92-10, 92B05, 92B15, 92C42
  • Format : Online Talk on Zoom
  • Author(s) :
    • David G Aragones (University of Castilla-La Mancha)
    • Gabriel F Calvo (University of Castilla-La Mancha)
    • Georgiana Crainiciuc (Spanish National Cardiovascular Research Center)
    • Miguel Palomino-Segura (Spanish National Cardiovascular Research Center)
    • Jon Sicilia (Spanish National Cardiovascular Research Center)
    • Andres Hidalgo (Yale University)