MapperPlus: Agnostic clustering of high-dimension data for precision medicine.

ABSTRACT: One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms tradi

SUBMITTER: Datta E 

PROVIDER: S-EPMC10411786 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature