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Machine Learning and GEMs

Machine and deep learning meet genome-scale metabolic modeling

Machine Learning and GEMs

Machine learning can be used together with genome-scale metabolic models (GEMs) to enhance our understanding of metabolic systems. 
Machine learning (ML) involves using algorithms that allow computers to identify patterns, learn from data, and make decisions or predictions without being explicitly programmed. In the context of biological research, ML is employed to analyze large-scale datasets, such as genomics, proteomics, and metabolomics, by uncovering hidden patterns, predicting outcomes, optimizing experimental design, finding metabolic pathways for biotechnology or therapeutic purposes, and personalized medicine.

Materials

Recommended Literature

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing, Weihua et al. (2017)

Machine and deep learning meet genome-scale metabolic modeling, Zampieri et al. (2019)

Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview, Sen et al. (2019)

Recent advances on constraint-based models by integrating machine learning, Rana et al. (2020)

The era of big data: Genome-scale modelling meets machine learning, Antonakoudis et al. (2020)

Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning, Medlock et al. (2020)

Leveraging genome-scale metabolic models for human health applications, Chowdhury et al. (2020)

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome, Esvap et al. (2021)

Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine, Heinken (2021)

Applications of machine learning in metabolomics: Disease modeling and classification, Gaial et al. (2022)

Reconstruction of compartmentalized genome-scale metabolic models using deep learning for over 800 fungi, Castillo et al. (2023)

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models, Turanli et al. (2024)

Improving genome-scale metabolic models of incomplete genomes with deep learning, Boer et al. (2024)