Statistical and machine learning techniques in human microbiome studies  Proposer: Dr Marcus Claesson (IE) m.claesson@ucc.ie   Funding period: 2019 – 2023 Summary In recent years, the human microbiome has been characterised in great detail in several large-scale studies as a key player in intestinal and non-intestinal diseases, e.g. inflammatory bowel disease, diabetes and liver cirrhosis, along with brain development and behaviour. As more associations between microbiome and phenotypes are elucidated, research focus is now shifting towards causality and clinical use for diagnostics, prognostics and therapeutics, where some promising applications have recently been showcased. Microbiome data are inherently convoluted, noisy and highly variable, and non-standard analytical methodologies are therefore required to unlock their clinical and scientific potential. While a range of statistical modelling and machine learning (ML) methods are now available, suboptimal implementation often leads to errors, overfitting and misleading results, due to a lack of good analytical practices and ML expertise in the microbiome community. Thus, we propose a COST Action network to create productive symbiosis between discoveryoriented microbiome researchers and data-driven ML experts, through regular meetings, workshops and training courses. Together, we will optimise then standardise the use of these techniques, following the creation of publically available benchmark datasets. Correct usage of these approaches will allow for better identification of predictive and discriminatory omics, features, more study repeatability, and provide mechanistic insights into the possible causal or contributing roles of the microbiome. We will also investigate automation opportunities and define priority areas for novel development of ML/statistics methods targeting microbiome data. Thus, this COST Action will open novel and exciting avenues within the fields of both ML/statistics and microbiome research.
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