ArticleComputational and structural biotechnology journal2025
MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues.European journal of immunology · 2025Review
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
- Decoding cardiac metabolic reprogramming through single-cell multi-omics: from mechanisms to therapeutic applications.Frontiers in cell and developmental biology · 2025Review
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Authors and funding
5 authors.
Funding
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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: these hybrid models combine the strengths of mechanistic and data-driven approaches, offering a promising platform for integrating different data sources with mechanistic knowledge. In this study, we present a Metabolic-Informed Neural Network (MINN) that utilizes multi-omics data to predict metabolic fluxes in
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.