During the 10th International Human Microbiome Consortium (IHMC) Congress 2024, held in Rome last June, Microbiomepost conducted an exclusive interview with engineer Corrado Vecchi, representing Ebris, who discussed the role of artificial intelligence (AI) in processing complex data generated from multi-omic studies. The exponential growth in the amount of data produced by high-throughput technologies, such as metabolomics, proteomics, and metagenomics, has necessitated the adoption of advanced computational approaches to efficiently analyze, categorize, and interpret this information.
The presented approach leverages convolutional neural networks and deep learning algorithms based on supervised learning, enabling the identification of complex patterns and relationships within the data. The process begins with data complexity reduction and normalization, ensuring that all variables are comparable. Once these steps are completed, deep learning models can be applied to uncover hidden correlations and insights that would be challenging to detect using traditional methods.
Corrado Vecchi illustrated the application of this method in a project focused on autism research, demonstrating how AI-based multi-omic analysis can help identify new biomarkers and molecular pathways involved in the disease. This approach can be extended to other fields in biomedicine, allowing for the analysis of large datasets in reduced timeframes and with greater precision, thus opening new avenues for research and personalized medicine.
The use of artificial intelligence, particularly deep learning, represents a pivotal component in next-generation biomedical research, providing innovative tools to tackle the complexity of biological data and support scientists in discovering new knowledge that could significantly impact the diagnosis and treatment of various conditions.
