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

Mathilde Sautreuil (1) , Sarah Lemler (1) , Paul-Henry P.-H. Cournède (1)

1 MICS - Mathématiques et Informatique pour la Complexité et les Systèmes

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.

Mots clés: Survival analysis; Neural networks; High-dimension; Cancer; Transcriptomics

Domaines: Statistiques [math.ST]

Mathilde Sautreuil, Sarah Lemler, Paul-Henry P.-H. Cournède. Neural networks to predict survival from RNA-seq data in oncology. 2021. ⟨hal-03224492⟩