TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification.

ABSTRACT:

Motivation

Learning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant; leading to a high-dimensional low-sample-size under-determined system. Moreover, microbiome data are often unbalanced and biased. Given such training data, machine learning methods often fail to perform a classification task with sufficient accuracy. Lack of signal is especially problematic when classes are represented in an unbalanced way in the training data; with some classes under-represented. The presence of inter-correlations among subsets of observations furt

SUBMITTER: Sayyari E 

PROVIDER: S-EPMC6612822 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature