DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes.

ABSTRACT: Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. DISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to evaluate patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against

SUBMITTER: Alexandre L 

PROVIDER: S-EPMC9581374 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature