scholarly journals Neural Network Modeling in Dithiothreitol Reduction and Ion Treatment of Recombinant Human Insulin Obtained from the Circular Dichroism (CD) Spectral Information

2009 ◽  
Vol 3 (1) ◽  
pp. 1-7
Author(s):  
Sardari Soroush ◽  
Soltani Saeed
Keyword(s):  
Neural Network ◽  
Human Insulin ◽  
Model Building ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Neural Network Modeling ◽  
Recombinant Human Insulin ◽  
Recombinant Peptide ◽  
Peptide Molecule ◽  
Artificial Neural Network Ann

In this study, induced changes in the secondary structure of the human insulin were carried out by addition of various reagents causing modification in the disulfide bond such as dithiothreitol (DTT) three dimensional structure of insulin. CD spectra were taken accordingly and the spectra recorded. There are methods to predict and estimate spectral changes of a peptide molecule, however there is no method to process CD spectral data and correlate them with that of inducing factor. Artificial intelligence backpropagation algorithm, as a strong model building tool was used here for prediction and data mining. Therefore, artificial neural network (ANN) methodology was used to build a model to study the effect of selected biochemical factors in the downstream process of a recombinant peptide.

Shut-in Stabilization Time Optimization for Better Reservoir Pressure Monitoring Harnessing Neural Network Modeling

2021 ◽  
Author(s):  
Mohammad Al Kadem ◽  
Ali Al Ssafwany ◽  
Ahmed Abdulghani ◽  
Hussain Al Nasir
Keyword(s):  
Neural Network ◽  
Neural Network Modeling ◽  
Stabilization Time ◽  
Pressure Derivative ◽  
Absolute Relative Error ◽  
Pressure Sensitive ◽  
Time Optimization ◽  
Average Absolute Relative Error ◽  
Study Results ◽  
Artificial Neural Network Ann

Abstract Stabilization time is an essential key for pressure measurement accuracy. Obtaining representative pressure points in build-up tests for pressure-sensitive reservoirs is driven by optimizing stabilization time. An artificial intelligence technique was used in the study for testing pressure-sensitive reservoirs using measuring gauges. The stabilization time function of reservoir characteristics is generally calculated using the diffusivity equation where rock and fluid properties are honored. The artificial neural network (ANN) technique will be used to predict the stabilization time and optimize it using readily available and known inputs or parameters. The values obtained from the formula known as the diffusion formula and the ANN technique are then compared against the actual values measured from pressure gauges in the reservoirs. The optimization of the number of datasets required to be fed to the network to allow for coverage over the whole range is essential as opposed to the clustering of the datasets. A total of about 3000 pressure derivative samples from the wells were used in the testing, training, and validation of the ANN. The datasets are optimized by dividing them into three fractional parts, and the number optimized through monitoring the ANN performance. The optimization of the stabilization time is essential and leads to the improvement of the ANN learning process. The sensitivity analysis proves that the use of the formula and ANN technique, compared to actual datasets, is better since, in the formula and ANN technique, the time was optimized with an average absolute relative error of 3.67%. The results are near the same, especially when the ANN technique undergoes testing using known and easily available parameters. Time optimization is essential since discreet points or datasets in the ANN technique and formula would not work, allowing ANN to work in situations of optimization. The study was expected to provide additional data and information, considering that stabilization time is essential in obtaining the pressure map representation. ANN is a superior technique and, through its superiority, allows for proper optimization of time as a parameter. Thus it can predict reservoir log data almost accurately. The method used in the study shows the importance of optimizing pressure stabilization time through reduction. The study results can, therefore, be applied in reservoir testing to achieve optimal results.

scholarly journals A SELF-ADAPTABLE DISTRIBUTED CBR VERSION OF THE EQUIVOX SYSTEM

2016 ◽  
Vol 28 (04) ◽  
pp. 1650028
Author(s):  
Julien Henriet ◽  
Christophe Lang ◽  
Ronnie Muthada Pottayya ◽  
Karla Breschi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Three Dimensional ◽  
Multi Agent System ◽  
Accidental Exposure ◽  
Interpolation Accuracy ◽  
Multi Agent ◽  
Artificial Neural Network Ann ◽  
Numerical Representations ◽  
Voxel Phantoms

Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.

Overall Structure Of The Heparin Molecule

1981 ◽  
Author(s):  
Edward Atkins
Keyword(s):  
Model Building ◽  
Specific Binding ◽  
Three Dimensional ◽  
Local Variation ◽  
Coagulation Cascade ◽  
Dimensional Structure ◽  
Chemical Analyses ◽  
X Ray Diffraction ◽  
First Order ◽  
Iduronic Acid

The polysaccharide suppresses the coagulation of blood by controlling the rate at which the plasma protein antithrombin inactivates the proteases of the coagulation cascade. One facet necessary in the full understanding of the interaction of heparin with antithrombin is the detailed shape of these macromolecules at an atomic resolution. X-ray diffraction methods are necessary to obtain the required information since the wavelength of the radiation is comparable with the distances between atoms. Analysis of the diffraction signals coupled with computerised model building and energy minimization procedures enables the three dimensional structure, or conformation, of the heparin molecule to be examined. A major part of the molecule is a repeating disaccharide of alternating 1.4-linked-2-deoxy-2-sulphamino-α-D-glucose-6-sulphate and 1.4-linked 2-sulphate-α-L-idopyranoslyluronic acid. This is a first order statement, made necessary to avoid confused and sometimes wrongful interpretations of past chemical analyses (which for many years suggested β-D-gluco-pyranosyluronic acid on the prominent uronic acid in heparin). More recent chemical analyses of selected heparins exhibiting high anticoagulent activity indicates that the idealised polydisaccharide repeat is perturned by local variation in the chemistry. The absence of a sulphate appendage on iduronic acid and the replacement of iduronic acid by glucuronic acid in the near vicinity are features proposed. The effect of these chemical modifications on the local conformation of the heparin molecule is considerable and suggest a specific binding site region within the heparin macromolecule amenable to interaction with antithrombin.

Predicting the Three-Dimensional Structure of Proteins by Homology-Based Model Building

1991 ◽  
pp. 137-158 ◽  
Author(s):  
Gerald M. Maggiora ◽  
S. Lakshmi Narasimhan ◽  
Cindy A. Granatir ◽  
James R. Blinn ◽  
Joseph B. Moon
Keyword(s):  
Model Building ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Structure Of Proteins

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

2012 ◽  
Vol 576 ◽  
pp. 91-94 ◽  
Author(s):  
Erry Yulian Triblas Adesta ◽  
Muataz H.F. Al Hazza ◽  
M.Y. Suprianto ◽  
Muhammad Riza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Temperature ◽  
Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

scholarly journals Artificial neural network for solving multi-parameter optimization problems

2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Test Functions ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Quality Of Approximation ◽  
And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

scholarly journals SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction

2021 ◽  
Vol 11 ◽  
Author(s):  
Shudong Wang ◽  
Dayan Liu ◽  
Mao Ding ◽  
Zhenzhen Du ◽  
Yue Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Binding Affinity ◽  
Three Dimensional ◽  
Model Performance ◽  
Protein Molecule ◽  
Stochastic Gradient Descent ◽  
Dimensional Structure ◽  
Scoring Functions ◽  
Ligand Complex ◽  
The Stability

Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein–ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein–drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein–molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and thus, our model is of acceptable robustness.

scholarly journals Reconstruction of 3D Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3D Models from ShapeNetCore Dataset

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1553 ◽  
Author(s):  
Audrius Kulikajevas ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Sanjay Misra
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
A Priori ◽  
Three Dimensional ◽  
Qualitative Evaluation ◽  
3D Models ◽  
Depth Sensors ◽  
The Neural Network ◽  
Artificial Neural Network Ann ◽  
Commercial Applications

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.

Consensus ensemble neural network multitarget model of RAGE inhibitory activity of chemical compounds

2021 ◽  
Vol 67 (3) ◽  
pp. 268-277
Author(s):  
P.M. Vassiliev ◽  
A.A. Spasov ◽  
A.N. Kochetkov ◽  
M.A. Perfilev ◽  
A.R. Koroleva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signaling Pathway ◽  
Inhibitory Activity ◽  
Three Dimensional ◽  
Neural Network Modeling ◽  
Target Proteins ◽  
Ensemble Neural Network ◽  
Dimensional Models ◽  
Three Dimensional Models

RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe complications in diabetes mellitus. RAGE inhibitors are promising pharmacological compounds that require the development of new predictive models. Based on the methodology of artificial neural networks, consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the level of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain was created on the basis of a previously created database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from a verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of sampling by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were shown. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.

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