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.

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.

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

scholarly journals A cyclic learning approach for improving pre-stack seismic processing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dario Augusto Borges Oliveira ◽  
Daniela Szwarcman ◽  
Rodrigo da Silva Ferreira ◽  
Semen Zaytsev ◽  
Daniil Semin
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Learning Approach ◽  
Noise Attenuation ◽  
Gas Industry ◽  
Seismic Processing ◽  
Machine Learning Approach ◽  
Testing And Evaluation ◽  
Selection Of

AbstractCurrent seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking. While many machine learning-based approaches have been proposed to support each of those steps, most of them disregard expert interactions to guide the overall optimization. This paper presents geocycles, a cyclic learning approach that mimics this iterative process, which can be applied to different pre-stack seismic processing tasks. Our method refactor these processes considering training, testing, and evaluation sub-tasks, which allow the selection of samples for greedy sequential processes targeting an overall optimum quality for very large seismic datasets. We present encouraging results showing that a cyclic structure and efficient quality metrics improved overall outcomes in up to 128% for two different seismic processing tasks in comparison to a 1-cycle machine learning approach.

scholarly journals A MACHINE LEARNING APPROACH FOR SELECTION OF POLYCYSTIC OVARIAN SYNDROME (PCOS) ATTRIBUTES AND COMPARING DIFFERENT CLASSIFIER PERFORMANCE WITH THE HELP OF WEKAAND PYCARET

2020 ◽  
pp. 59-63
Author(s):  
Ashok Munjal ◽  
Rekha Khandia ◽  
Brijraj Gautam
Keyword(s):  
Machine Learning ◽  
Polycystic Ovary ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Signs And Symptoms ◽  
Medical Professional ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Disease Condition ◽  
Selection Of

For any medical treatment there is a requirement of identification of features those are affecting the clinical condition the most. These are the parameters which decide the line of treatment and decide prognostic values. Process of diagnosis includes various aspects like physical examination through symptoms exhibited for a disease, person’s previous medical history, and various type of medical tests. Diagnosis of a disease is often challenging since there are many nonspecific signs and symptoms and often are common with other ailments too. In present study we applied Advance Machine Learning approach to identify the major attributes those are involved in polycystic ovary syndrome (PCOS) disease progression as well as help medical professional to predict the disease with accuracy and minimal time. Present work encompasses the use of genetic algorithm a Machine learning approach for selection of major attributes (the sign and symptoms ) for PCOS patients data which affect the disease condition most ,in present study various classifiers have been applied in our dataset and different accuracy parameters also have been used including Confusion matrix , Precision , F1 score and AUC (area under the curve) to select the best classifier which classify the diseased and non diseased patients with high accuracy.

Machine Learning Approach for Predicting Nucleophilicity of Organic Molecules

2021 ◽  
Author(s):  
Vaneet Saini ◽  
Aditya Sharma ◽  
Dhruv Nivatia
Keyword(s):  
Machine Learning ◽  
Experimental Determination ◽  
Organic Molecules ◽  
Chemical Reactivity ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Intensive Approach

Nucleophilicity provides an important information about chemical reactivity of organic molecules. Experimental determination of nucleophilicity parameter is tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that...

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
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