Survival prediction and risk estimation of Glioma patients using mRNA expressions

TL;DR

This paper introduces a novel probabilistic programming approach for predicting glioma patient survival using gene expression data, achieving higher accuracy than traditional methods and providing a risk estimation model.

Contribution

It presents a new probabilistic programming method for glioma survival prediction and develops a prognostic risk model based on gene signatures.

Findings

Achieved 74% accuracy in survival prediction

Identified a 7-gene signature related to survival

Developed a risk estimation model reflecting patient prognosis

Abstract

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.