Learning Individual Survival Models from PanCancer Whole Transcriptome Data.

ABSTRACT:

Purpose

Personalized medicine attempts to predict survival time for each patient, based on their individual tumor molecular profile. We investigate whether our survival learner in combination with a dimension reduction method can produce useful survival estimates for a variety of patients with cancer.

Experimental design

This article provides a method that learns a model for predicting the survival time for individual patients with cancer from the PanCancer Atlas: given the (16,335 dimensional) gene expression profiles from 10,173 patients, each having one of 33 cancers, this method uses unsupervised nonnegative matrix factorization (NMF) to reexpress the gene expression data for each patient in terms of 100 learned NMF factors. It then feeds these 100 factors into the Multi

SUBMITTER: Kumar N 

PROVIDER: S-EPMC10543961 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature