The number of microbiome-related studies has notably increased the availability of data on
human microbiome composition and function. These studies provide the essential material …

Abstract Machine learning algorithms have been applied widely in the fields of natural
science, social science and engineering. It can be expected that machine learning …

The COVID-19 era has profoundly affected everyday human life, the environment, and
freshwater ecosystems worldwide. Despite the numerous influences, a strict COVID-19 …

Microbiome and omics datasets are, by their intrinsic biological nature, of high
dimensionality, characterized by counts of large numbers of components (microbial genes …

In the last twenty years, the life sciences have seen a dramatic increase in the size and
number of data sets. Simple sensing devices in many cases offer real-time data streaming …

Modern applications in chemometrics and bioinformatics result in compositional data sets
with a high proportion of zeros. An example are microbiome data, where zeros refer to …

Compositional data are nonnegative data carrying relative, rather than absolute, information—
these are often data with a constant-sum constraint on the sample values, for example …

The log-ratio approach to compositional data (CoDa) analysis has now entered a mature
phase. The principles and statistical tools introduced by J. Aitchison in the eighties have …

Although metagenomic sequencing is now the preferred technique to study microbiome-host
interactions, analyzing and interpreting microbiome sequencing data presents challenges …

The use of next-generation diagnostic tools to optimise the anaerobic digestion of municipal
sewage sludge has the potential to increase renewable natural gas recovery, improve the …