Core-influencer gut microbiome identification

Abstract

The human gut microbiome exhibits remarkable diversity among individuals, yet numerous studies have identified the presence of several bacteria consistently observed across different geographical locations. This research aims to ascertain a definitive set of gut bacteria and utilize them to develop a method capable of distinguishing between healthy and diseased individuals. Given the intricate interconnections among these bacteria, it is imperative to establish concrete evidence to characterize them as the universal core bacteria. This investigation encompasses a machine learning approach, analysis of longitudinal microbiome data, and evaluation of functional similarities. To distinguish diseases from control states, distinct subsets of studies relevant to specific conditions will be engineered. Subsequently, a core score will be generated, providing valuable insights into the likelihood of a given sample being in a healthy state or a particular disease condition. By combining multiple analytical approaches, this research endeavours to advance our understanding of the gut microbiome's role in health and disease, potentially paving the way for personalized diagnostic and therapeutic interventions. The findings of this study hold promise in unravelling the complex associations between gut bacteria and human health, leading to improved disease management and precision medicine strategies.