AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Metabolic Networks and Pathways

Showing 11 to 20 of 113 articles

Clear Filters

A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.

PloS one
The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to pre...

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

Methods in molecular biology (Clifton, N.J.)
The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires larg...

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Microbiology spectrum
Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse pr...

Machine learning enables identification of an alternative yeast galactose utilization pathway.

Proceedings of the National Academy of Sciences of the United States of America
How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fung...

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.

Molecular biotechnology
Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatmen...

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.

Microbial cell factories
BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have...

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

Molecular omics
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequenci...

Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.

Journal of bioinformatics and computational biology
Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of dr...

BPP: a platform for automatic biochemical pathway prediction.

Briefings in bioinformatics
A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coale...

Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis.

ACS chemical neuroscience
Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based...