A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery.

ABSTRACT: Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing

SUBMITTER: Rintala TJ 

PROVIDER: S-EPMC8575038 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature