Pancancer survival prediction using a deep learning architecture with multimodal representation and integration.

ABSTRACT:

Motivation

Use of multi-omics data carrying comprehensive signals about the disease is strongly desirable for understanding and predicting disease progression, cancer particularly as a serious disease with a high mortality rate. However, recent methods currently fail to effectively utilize the multi-omics data for cancer survival prediction and thus significantly limiting the accuracy of survival prediction using omics data.

Results

In this work, we constructed a deep learning model with multimodal representation and integration to predict the survival of patients using multi-omics data. We first developed an unsupervised learning part to extract high-level feature representations from omics data of different modalities. Then, we used an attention-based method to integrate f

SUBMITTER: Fan Z 

PROVIDER: S-EPMC9945067 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature