Information-incorporated sparse convex clustering for disease subtyping.

ABSTRACT:

Motivation

Heterogeneity in human diseases presents clinical challenges in accurate disease characterization and treatment. Recently available high throughput multi-omics data may offer a great opportunity to explore the underlying mechanisms of diseases and improve disease heterogeneity assessment throughout the treatment course. In addition, increasingly accumulated data from existing literature may be informative about disease subtyping. However, the existing clustering procedures, such as Sparse Convex Clustering (SCC), cannot directly utilize the prior information even though SCC produces stable clusters.

Results

We develop a clustering procedure, information-incorporated Sparse Convex Clustering, to respond to the need for disease subtyping in precision medicine. Utili

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10329496 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature