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Metabolic Networks and Pathways

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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...

A Network Integration Method for Deciphering the Types of Metabolic Pathway of Chemicals with Heterogeneous Information.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: A metabolic pathway is an important type of biological pathway, which is composed of a series of chemical reactions. It provides essential molecules and energies for living organisms. To date, several metabolic pathways have been u...

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...

Reactome diagram viewer: data structures and strategies to boost performance.

Bioinformatics (Oxford, England)
MOTIVATION: Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. He...

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...

Discriminating early- and late-stage cancers using multiple kernel learning on gene sets.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-sta...

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...

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...

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...