# Survival prediction and risk estimation of Glioma patients using mRNA expressions

arXiv: https://arxiv.org/abs/2011.00659 · HTML: https://mlsanity.com/paper/2011.00659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction](https://mlsanity.com/paper/2311.07033/) (2023)

## Abstract (arXiv)

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.