DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data

DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data

Abstract Background Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible—therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, a...

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Bibliographic Details
Main Authors: Grzegorz Mrukwa, Joanna Polanska
Format: Article
Language:English
Published: BMC 2022-12-01
Series:BMC Bioinformatics
Subjects:
Machine learning
Unsupervised clustering
Feature engineering
Local feature space adaptation
High-dimensional data analysis
Omics
Online Access:https://doi.org/10.1186/s12859-022-05093-z

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