Survival prediction and risk estimation of Glioma patients using mRNA expressions BIBE 2020

Gliomas are lethal type of central nervous system
tumors with a poor prognosis. Recently, with the advancements in
the micro-array technologies thousands of gene expression related
data of glioma patients are acquired, leading for salient analysis
in many aspects. Thus, genomics are been emerged into the field
of prognosis analysis. In this work, we identify survival related 7
gene signature and explore two approaches for survival prediction
and risk estimation. For survival prediction, we propose a novel
probabilistic programming based approach, which outperforms
the existing traditional machine learning algorithms. An average
4 fold accuracy of 74% is obtained with the proposed algorithm.
Further, we construct a prognostic risk model for risk estimation
of glioma patients. This model reflects the survival of glioma
patients, with high risk for low survival patients.