Claire Jean-Quartier1, Fleur Jeanquartier2,3, Aydin Ridvan4, Matthias Kargl4, Tica Mirza4, Tobias Stangl4, Robi Markaĉ4, Mauro Jurada4, Andreas Holzinger4. 1. Human-Centered AI Lab (Holzinger Group), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria. 2. Human-Centered AI Lab (Holzinger Group), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria. fleur.jeanquartier@tugraz.at. 3. Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria. fleur.jeanquartier@tugraz.at. 4. Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria.
Abstract
BACKGROUND: Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age. METHODS: In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers. RESULTS: Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification. CONCLUSIONS: We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.
BACKGROUND:Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age. METHODS: In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers. RESULTS: Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification. CONCLUSIONS: We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.
Entities:
Keywords:
Age clusters; Glioma classification; IDH1; K-Means; Random Forest; XAI; explainable artificial intelligence; pediatric cancer
Authors: Cassie N Kline; Nancy M Joseph; James P Grenert; Jessica van Ziffle; Iwei Yeh; Boris C Bastian; Sabine Mueller; David A Solomon Journal: Neuro Oncol Date: 2016-02-21 Impact factor: 12.300
Authors: Sherise D Ferguson; Joanne Xiu; Shiao-Pei Weathers; Shouhao Zhou; Santosh Kesari; Stephanie E Weiss; Roeland G Verhaak; Raymond J Hohl; Geoffrey R Barger; Sandeep K Reddy; Amy B Heimberger Journal: Oncotarget Date: 2016-10-25