Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Jean-Quartier, C; Jeanquartier, F; Ridvan, A; Kargl, M; Mirza, T; Stangl, T; Markaĉ, R; Jurada, M; Holzinger, A.
Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.
BMC MED INFORM DECIS. 2021; 21(1): 77-77.
Doi: 10.1186/s12911-021-01420-1
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- Leading authors Med Uni Graz
- Jean-Quartier Claire
- Co-authors Med Uni Graz
- Holzinger Andreas
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- Abstract:
- Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age. 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. Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification. We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.
- Find related publications in this database (using NLM MeSH Indexing)
- Biomarkers, Tumor - genetics
- Cluster Analysis -
- Glioma - diagnosis
- Glioma - genetics
- Humans -
- Isocitrate Dehydrogenase - genetics
- Mutation -
- Find related publications in this database (Keywords)
- Glioma classification
- pediatric cancer
- explainable artificial intelligence
- XAI
- Age clusters
- K-Means
- Random Forest
- IDH1