MoSBi: Automated signature mining for molecular stratification and subtyping.

ABSTRACT: The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates res

SUBMITTER: Rose TD 

PROVIDER: S-EPMC9169782 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature