AIMC Topic: Metabolic Networks and Pathways

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Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

Communications biology
The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites ar...

Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning.

ACS synthetic biology
The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain developm...

Flux prediction using artificial neural network (ANN) for the upper part of glycolysis.

PloS one
The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexi...

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks.

Journal of chemical information and modeling
Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN ) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes...

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Nature communications
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machi...

Mining features for biomedical data using clustering tree ensembles.

Journal of biomedical informatics
The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets...

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

PLoS computational biology
Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN frame...

Gas chromatography-mass spectrometry metabolomic study of lipopolysaccharides toxicity on rat basophilic leukemia cells.

Chemico-biological interactions
Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production, fatal sepsis syndrome and depression/multiple organ failure, as pathophysiologic demonstration. Various toxic effects of LPS have been extensively reported, mainly on the toxici...

Data mining and pathway analysis of glucose-6-phosphate dehydrogenase with natural language processing.

Molecular medicine reports
Human glucose-6-phosphate dehydrogenase (G6PD) is a crucial enzyme in the pentose phosphate pathway, and serves an important role in biosynthesis and the redox balance. G6PD deficiency is a major cause of neonatal jaundice and acute hemolyticanemia, ...