This article provides a comprehensive analysis of enzyme-constrained genome-scale metabolic models (ecGEMs), which enhance traditional flux balance analysis by incorporating enzymatic turnover and proteomic limitations.
This article provides a comprehensive framework for researchers and drug development professionals on validating metabolite changes across disease progression stages.
Graph Neural Networks (GNNs) are emerging as a transformative technology for metabolomics, offering powerful capabilities to interpret complex molecular data and predict metabolic behaviors.
Accurate measurement of resting metabolic rate (RMR) is critical for nutritional intervention and metabolic research.
This article provides a systematic evaluation of predictive equations for resting energy expenditure (REE), addressing critical needs in research and clinical practice.
This article provides a comprehensive comparison of machine learning (ML) models for metabolic prediction, addressing key needs of researchers and drug development professionals.
This comprehensive review analyzes the accuracy, applicability, and limitations of current basal metabolic rate (BMR) assessment methodologies for researchers and drug development professionals.
This article provides a systematic guide for researchers and drug development professionals on the validation of metabolic biomarkers using Enzyme-Linked Immunosorbent Assay (ELISA) techniques.
Accurate assessment of Basal Metabolic Rate (BMR) is fundamental for nutritional science, metabolic research, and informing drug development for metabolic disorders.
This article provides a comprehensive guide for researchers and drug development professionals on cross-validating targeted and untargeted metabolomics results.