Predictive Model for Selection of Upper Treated Vertebra Using a Machine Learning Approach

2020 ◽  
Author(s):  
Renaud Lafage ◽  
Bryan Ang ◽  
Basel Sheikh Alshabab ◽  
Jonathan Elysee ◽  
Francis Lovecchio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Selection Of

P84. Predictive model for selection of upper treated vertebra using a machine learning approach

2020 ◽  
Vol 20 (9) ◽  
pp. S187
Author(s):  
Renaud Lafage ◽  
Basel Sheikh Alshabab ◽  
Jonathan Elysee ◽  
Francis C. Lovecchio ◽  
Karen Weissmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Selection Of

scholarly journals A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization

Catalysts ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 291 ◽  
Author(s):  
Anamya Ajjolli Nagaraja ◽  
Philippe Charton ◽  
Xavier F. Cadet ◽  
Nicolas Fontaine ◽  
Mathieu Delsaut ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glass Ceiling ◽  
Principal Component ◽  
Enzyme Concentration ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Free System ◽  
Machine Learning Approach ◽  
Selection Of

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of “glass ceiling”. In order to explore this “glass ceiling” space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the “out-of-the-box” fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.

scholarly journals Predicting the chemical reactivity of organic materials using a machine-learning approach

2020 ◽  
Vol 11 (30) ◽  
pp. 7813-7822 ◽  
Author(s):  
Byungju Lee ◽  
Jaekyun Yoo ◽  
Kisuk Kang
Keyword(s):  
Machine Learning ◽  
Organic Materials ◽  
Chemical Reactivity ◽  
Functional Materials ◽  
Chemical Components ◽  
Learning Approach ◽  
System P ◽  
Machine Learning Approach ◽  
Selection Of

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.

Sign in / Sign up

Export Citation Format

Share Document