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Machine Learning (ML) Applied to Microbiome Genetic Mapping
This work has shown that artificial intelligence can be used to predict host phenotypes based on feature selection informed by taxonomy to establish an association between the microbiome and individual characteristics and this relationship can be a proxy to predict various disease states and improve human health.
Abstract
Gut microbiome (MBI) can be understood as an ecosystem where all organisms can interact with each other and with the environment. In a healthy individual, pathogenic and symbiotic microbiota coexist without problems. However, the MBI is dynamic and its changes are influenced by different factors, such as diet, antibiotic use, disease, and others. If there is a disturbance in this balance, dysbiosis occurs, disrupting these normal interactions. As a result, the body can become more susceptible to disease. Changes in MBI can impact the immune system and the quality of life of the individual. Artificial intelligence can be used to predict host phenotypes based on feature selection informed by taxonomy to establish an association between the microbiome and individual characteristics. This relationship can be a proxy to predict various disease states and improve human health.