Microbiome And Machine Learning Volume Ii

Microbiome and Machine Learning, Volume II

Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.

New Techniques in Microbiome Research - Vol II – Host-Microbiome Interactions using ‘meta-omics’ techniques

Given the success of Volume I of this Research Topic, we are pleased to announce the launch of Volume II New Techniques in Microbiome Research. Microbiome research is driven by an interest in the mechanism of how the microbiota impacts and respond to disease. To date, only a small percentage of the bacteria that comprise the human microbiome have been isolated, identified, and studied. More than 95% of microorganisms have not been isolated and cultured because the required growth conditions cannot be reproduced in the laboratory. However, recent technological advances in next-generation sequencing and metagenomics have made it feasible to analyze the entire human microbiome. Improvement in technology has made it possible to culture the ‘unculturable’ human microbiota, isolate, and identify the anaerobes. For example, human noroviruses have been replicated in stem cell-derived human enteroids, complex microbiota have been co-cultured in an anaerobic intestine-on-a-chip system, a novel human enteroid-anaerobe co-culture system has been used to study microbial-host interaction under physiological hypoxia, and microbial communities have been cultured from fecal samples using mini-bioreactor arrays.

Computational methods for microbiome analysis, volume 2