MoSBi: Automated signature mining for molecular stratification and subtyping

Abstract excerpt

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 simu...

Topics

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• Computational Biology
• Phenotype
• Radiomics and Machine Learning in Medical Imaging
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

58e2cd36-edea-57d3-8776-d9a54728f8db

DOI

10.1101/2021.09.30.462567