Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications.

ABSTRACT: Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strat

SUBMITTER: Giulia A 

PROVIDER: S-EPMC9580939 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature