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/

Zendy Plus

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

Zendy Tools

Zendy Open

Cancer is a complex disease with significant social and economic impact.Advancements in high-throughput molecular assays and the reduced cost forperforming high-quality multi-omics measurements have fuelled insights throughmachine learning . Previous studies have shown promise on using multiple omiclayers to predict survival and stratify cancer patients. In this paper, wedeveloped a Supervised Autoencoder (SAE) model for survival-based multi-omicintegration which improves upon previous work, and report a Concrete SupervisedAutoencoder model (CSAE), which uses feature selection to jointly reconstructthe input features as well as predict survival. Our experiments show that ourmodels outperform or are on par with some of the most commonly used baselines,while either providing a better survival separation (SAE) or being moreinterpretable (CSAE). We also perform a feature selection stability analysis onour models and notice that there is a power-law relationship with featureswhich are commonly associated with survival. The code for this project isavailable at: https://github.com/phcavelar/coxae

Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders