MMOSurv: meta-learning for few-shot survival analysis with multi-omics data.

ABSTRACT:

Motivation

High-throughput techniques have produced a large amount of high-dimensional multi-omics data, which makes it promising to predict patient survival outcomes more accurately. Recent work has showed the superiority of multi-omics data in survival analysis. However, it remains challenging to integrate multi-omics data to solve few-shot survival prediction problem, with only a few available training samples, especially for rare cancers.

Results

In this work, we propose a meta-learning framework for multi-omics few-shot survival analysis, namely MMOSurv, which enables to learn an effective multi-omics survival prediction model from a very few training samples of a specific cancer type, with the meta-knowledge across tasks from relevant cancer types. By assuming a deep C

SUBMITTER: Wen G 

PROVIDER: S-EPMC11673192 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature