Gut microbiota and artificial intelligence approaches: A scoping review

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances

Recent Advances in the Phylogenetic Analysis to Study Rumen Microbiome

Background: Recent rumen microbiome studies are progressive due to the advent of next-generation sequencing technologies, computational models, and gene referencing databases. Rumen metagenomics enables the linking of the genetic structure and composition of the rumen microbial community to the functional role it plays in the ecosystem. Systematic investigations of the rumen microbiome, including its composition in cattle, have revealed the importance of microbiota in rumen functions. Various research studies have identified different types of microbiome species that reside within the rumen and their relationships, leading to a greater understanding of their functional contribution. Objective: The objective of this scoping review was to highlight the role of the phylogenetic and functional composition of the microbiome in cattle functions. It is driven by a natural assumption that closely related microbial genes/operational taxonomical units (OTUs)/amplicon sequence variants (ASVs) by phylogeny are highly correlated and tend to have similar functional traits. Methods: PRISMA approach has been used to conduct the current scoping review providing state-of-the-art studies for a comprehensive understanding of microbial genes’ phylogeny in the rumen microbiome and their functional capacity. Results: 44 studies have been included in the review, which has facilitated phylogenetic advancement in studying important cattle functions and identifying key microbiota. Microbial genes and their inter-relations have the potential to accurately predict the phenotypes linked to ruminants, such as feed efficiency, milk production, and high/low methane emissions. In this review, a variety of cattle have been considered, ranging from cows, buffaloes, lambs, Angus Bulls, etc. Also, results from the reviewed literature indicate that metabolic pathways in microbiome genomic groupings result in better carbon channeling, thereby affecting methane production by ruminants. Conclusion: The mechanistic understanding of the phylogeny of the rumen microbiome could lead to a better understanding of ruminant functions. The composition of the rumen microbiome is crucial for the understanding of dynamics within the rumen environment. The integration of biological domain knowledge with functional gene activity, metabolic pathways, and rumen metabolites could lead to a better understanding of the rumen system