Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques.

ABSTRACT: The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoi

SUBMITTER: Tandon D 

PROVIDER: S-EPMC4849775 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature