Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). GlioSurvML is a machine learning classifier built to predict the survival group of glioblastoma patients based on transcriptomics. Please refer to the publication for more details here.

The current web tool includes 2 ML classifiers

  1. based on gene expression profiles
  2. based on gene expression profiles along with “age at diagnosis (in yrs)” information
  • The method needs RMA normalized and log2 transformed gene expression profiles of Affymetrix microarray platform
  • Table must contain Samples as columns and gene symbols as rows. The sample input files are given.
  • The “age at diagnosis” information is read as years.

At present, the classifier is built for Affymetrix U133 plus 2 array data. The classifier for RNA-seq data will be developed and updated in the future.

"Nothing in life is to be feared; it is only to be understood." - Marie Sklodowska Curie

Try analysis

Gene Expression Platform
Patient characteristics available

Disclaimer: This tool is intended for research purposes only and in no way to substitute professional medical advice, consultation, diagnosis, or treatment. Neither the authors nor the hospital guarantee the accuracy of its calculations for any particular patient. These services provide no warranties, express or implied and shall not be liable for any direct, consequential, lost profits, or other damages incurred by the user of this information tool. Please refer to the technical reports and publications cited in this website for further information.


Kalya, M.; Kel, A.; Leha, A.; Altynbekova, K.; Wingender, E.; Beissbarth, T. Machine Learning based Survival Group Prediction in Glioblastoma . Preprints 2022, 2022020051 (doi: 10.20944/preprints202202.0051.v1).

Maintained by © 2022 GeneXplain GmbH Author Manasa Kalya Purushothama