AIMC Topic: Metabolic Networks and Pathways

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Machine and deep learning meet genome-scale metabolic modeling.

PLoS computational biology Jul 11, 2019
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine lea...
Metabolic Networks and Pathways Genome Machine Learning Phenotype Deep Learning Genotype Computational Biology
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HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Scientific reports Jul 3, 2019
Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have bee...
Artificial Intelligence Databases, Factual Diet Humans Drug Repositioning Metabolic Networks and Pathways Neoplasms Food Antineoplastic Agents Food Analysis
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Recognition of early and late stages of bladder cancer using metabolites and machine learning.

Metabolomics : Official journal of the Metabolomic Society Jun 20, 2019
INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and ...
Humans Machine Learning ROC Curve Urinary Bladder Neoplasms Metabolic Networks and Pathways Software Biomarkers, Tumor Insulin ErbB Receptors Proto-Oncogene Proteins c-akt Area Under Curve Glycine Neoplasm Staging Galactose Amino Acids
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Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

Communications biology Jun 18, 2019
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...
Metabolomics Metabolic Networks and Pathways Solanum lycopersicum Machine Learning
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Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma.

Scientific reports May 28, 2019
Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a singl...
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Multiple Myeloma Datasets as Topic Metabolic Networks and Pathways Bone Marrow Immunoglobulins Case-Control Studies Metabolome Principal Component Analysis Artificial Intelligence Female Aged Aged, 80 and over Humans Male Neural Networks, Computer Middle Aged
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Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning.

ACS synthetic biology May 24, 2019
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...
Dodecanol Metabolic Networks and Pathways Synthetic Biology Metabolic Engineering Machine Learning Algorithms Escherichia coli
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Flux prediction using artificial neural network (ANN) for the upper part of glycolysis.

PloS one May 8, 2019
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...
Metabolic Networks and Pathways NAD Metabolic Flux Analysis Neural Networks, Computer Algorithms Glycolysis
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Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks.

Journal of chemical information and modeling Mar 4, 2019
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 Anti-Bacterial Agents Microbial Sensitivity Tests Enterococcus faecium Metabolic Networks and Pathways Models, Biological Acinetobacter baumannii
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Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Nature communications Dec 7, 2018
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...
Algorithms Proteome Escherichia coli Machine Learning Metabolic Networks and Pathways Kinetics Models, Biological Biocatalysis Escherichia coli Proteins
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Mining features for biomedical data using clustering tree ensembles.

Journal of biomedical informatics Jul 29, 2018
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...
Gene Regulatory Networks Data Mining Cluster Analysis Algorithms Computational Biology Saccharomyces cerevisiae Decision Trees Databases, Factual Metabolic Networks and Pathways Humans Escherichia coli Protein Interaction Maps Machine Learning
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