MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.

ABSTRACT: The understanding of cellular behavior relies on the integration of metabolism and its regulation. Multi-omics data provide a detailed snapshot of the molecular processes underpinning cellular functions and their regulation, describing the current state of the cell. While Machine Learning (ML) models can uncover complex patterns and relationships within these data, they require large datasets for training and often lack interpretability. On the other hand, mathematical models, such as Genome-Scale Metabolic Models (GEMs), offer a structured framework for analyzing the organization and dynamics of specific cellular mechanisms. At the same time, they don't allow for seamless integration of omics information. Recently, a new framework to embed GEMs in a neural network has been introduced: the

SUBMITTER: Tazza G 

PROVIDER: S-EPMC12359237 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature