ML-based predictive gut microbiome analysis for health assessment

Personalized medicine is a rapidly evolving field to which many resources have been devoted recently. It represents a paradigm shift from a one-size-fits-all approach to healthcare, focusing instead on tailoring treatments and diagnoses to individual patients. This study aims to contribute to this transition by leveraging recent advancements in microbiome research. An earlier study that computed the Gut Microbiome Health Index (GMHI), a potent indicator capable of predicting disease presence with approximately 70% of accuracy, serves as the foundation for this research. The GMHI is a numerical value that classifies a person as healthy if the index is greater than zero, non-healthy if less than zero, and undetermined if the GMHI is equal to zero. The intent of this study is to further utilize and develop this index using two different architectures a Fully Connected NN and an Autoencoder NN and to apply those techniques to a new and distinct dataset containing information about individuals affected by COVID-19.

GIL SORRIBES Manel;  LEONI Gabriele;  PUERTAS GALLARDO Antonio;  PETRILLO Mauro;  CONSOLI Sergio;  GÓMEZ Vicenç;  CERESA Mario; 

2024-08-02

Elsevier BV

JRC134262

1877-0509 (online),   

https://www.sciencedirect.com/science/article/pii/S1877050924015618,    https://publications.jrc.ec.europa.eu/repository/handle/JRC134262,   

10.1016/j.procs.2024.06.318 (online),