Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

• Published in: PLoS computational biology Vol. 20; no. 6; p. e1011361
• Main Authors: VanderDoes, Jeremy, Marceaux, Claire, Yokote, Kenta, Asselin-Labat, Marie-Liesse, Rice, Gregory, Hywood, Jack D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.06.2024; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Breast cancer; Cancer; Cancer therapies; Care and treatment; Cell culture; Cell interaction; Cell interactions; Data analysis; Datasets; Development and progression; Expected values; Functionals; Geospatial data; Immune system; Immunotherapy; Information management; Ion beams; Lung cancer; Machine learning; Medical research; Medicine and Health Sciences; Medicine, Experimental; Methods; Microenvironments; Neutrophils; Physical Sciences; Predictions; Prognosis; Research and Analysis Methods; Scholarships & fellowships; Spatial data; Spatial distribution; Statistics; Tumor cells; Tumor microenvironment; Tumors; Canada

Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this...