English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cancer survival prediction is important for developing personalizedtreatments and inducing disease-causing mechanisms. Multi-omics dataintegration is attracting widespread interest in cancer research for providinginformation for understanding cancer progression at multiple genetic levels.Many works, however, are limited because of the high dimensionality andheterogeneity of multi-omics data. In this paper, we propose a novel method tointegrate multi-omics data for cancer survival prediction, called StackedAutoEncoder-based Survival Prediction Neural Network (SAEsurv-net). In thecancer survival prediction for TCGA cases, SAEsurv-net addresses the curse ofdimensionality with a two-stage dimensionality reduction strategy and handlesmulti-omics heterogeneity with a stacked autoencoder model. The two-stagedimensionality reduction strategy achieves a balance between computationcomplexity and information exploiting. The stacked autoencoder model removesmost heterogeneities such as data's type and size in the first group ofautoencoders, and integrates multiple omics data in the second autoencoder. Theexperiments show that SAEsurv-net outperforms models based on a single type ofdata as well as other state-of-the-art methods.

Stacked Autoencoder Based Multi-Omics Data Integration for Cancer Survival Prediction