Neural networks to predict survival from RNA-seq data in oncology

Mathilde Sautreuil; Sarah Lemler; Paul-Henry Cournède

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arXiv · 2105.08338

Neural networks to predict survival from RNA-seq data in oncology

Mathilde Sautreuil·Sarah Lemler·Paul-Henry Cournède

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Abstract

Survival analysis consists of studying the elapsed time until an event of interest, such as the death or recovery of a patient in medical studies. This work explores the potential of neural networks in survival analysis from clinical and RNA-seq data. If the neural network approach is not recent in survival analysis, methods were classically considered for low-dimensional input data. But with the emergence of high-throughput sequencing data, the number of covariates of interest has become very large, with new statistical issues to consider. We present and test a few recent neural network approaches for survival analysis adapted to high-dimensional inputs.

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BibTeX RIS

Mathilde Sautreuil, Sarah Lemler, Paul-Henry Cournède. 2021-05-18. Neural networks to predict survival from RNA-seq data in oncology. https://arxiv.org/abs/2105.08338

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