scholarly journals Prediction and Characterization of Missing Proteomic Data inDesulfovibrio vulgaris

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Feng Li ◽  
Lei Nie ◽  
Gang Wu ◽  
Jianjun Qiao ◽  
Weiwen Zhang
Keyword(s):  
Neural Network ◽  
Multivariate Logistic Regression Analysis ◽  
Network Models ◽  
Mrna Abundance ◽  
Biological Information ◽  
Desulfovibrio Vulgaris ◽  
Neural Network Models ◽  
Proteomic Data ◽  
Aliphatic Index ◽  
Trained Neural Network

Proteomic datasets are often incomplete due to identification range and sensitivity issues. It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems. In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets ofDesulfovibrio vulgaris, and to predict protein abundance for the proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts. The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several previous regression models. The validity of the trained neural network model was evaluated using biological information (i.e. operons). To seek understanding of mechanisms causing missing proteomic data, we used a multivariate logistic regression analysis and the result suggested that some key factors, such as protein instability index, aliphatic index, mRNA abundance, effective number of codons () and codon adaptation index (CAI) values may be ascribed to whether a given expressed protein can be detected. In addition, we demonstrated that biological interpretation can be improved by use of imputed proteomic datasets.

Prediction of Hardness Distribution in Plasma Arc Surface Hardening Using Neural Network

2017 ◽  
pp. 318-333
Author(s):  
Manoj Kumar
Keyword(s):  
Neural Network ◽  
Surface Hardening ◽  
Regression Models ◽  
Network Models ◽  
Hardness Distribution ◽  
Plasma Arc ◽  
Statistical Regression ◽  
Neural Network Models ◽  
The Neural Network ◽  
Trained Neural Network

In this chapter, an attempt has been made to develop neural network models to predict the hardness distribution of hardened zone in plasma arc surface hardening process. The back propagation method with the Levenberg-Marquardt algorithm was used to train the neural network models. Hardness distributions were collected by the experimental setup in the laboratory and the associated data were used to train the neural network models. Furthermore, the prediction of neural network models were compared with those obtained from a statistical regression models. It is confirmed experimentally that the hardness distribution can be accurately predicted by the trained neural network models. The accuracy of hardness distribution prediction using neural network is superior to that using other statistical regression models.

scholarly journals CDeep3M-Preview: Online segmentation using the deep neural network model zoo

2020 ◽  
Author(s):  
Matthias G Haberl ◽  
Willy Wong ◽  
Sean Penticoff ◽  
Jihyeon Je ◽  
Matthew Madany ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Network Models ◽  
Large Datasets ◽  
Neural Network Models ◽  
Online Tool ◽  
Trained Neural Network ◽  
Cloud Applications ◽  
Microscopy Images

AbstractSharing deep neural networks and testing the performance of trained networks typically involves a major initial commitment towards one algorithm, before knowing how the network will perform on a different dataset. Here we release a free online tool, CDeep3M-Preview, that allows end-users to rapidly test the performance of any of the pre-trained neural network models hosted on the CIL-CDeep3M modelzoo. This feature makes part of a set of complementary strategies we employ to facilitate sharing, increase reproducibility and enable quicker insights into biology. Namely we: (1) provide CDeep3M deep learning image segmentation software through cloud applications (Colab and AWS) and containerized installations (Docker and Singularity) (2) co-hosting trained deep neural networks with the relevant microscopy images and (3) providing a CDeep3M-Preview feature, enabling quick tests of trained networks on user provided test data or any of the publicly hosted large datasets. The CDeep3M-modelzoo and the cellimagelibrary.org are open for contributions of both, trained models as well as image datasets by the community and all services are free of charge.

scholarly journals Speedy Image Crowd Counting by Light Weight Convolutional Neural Network

2021 ◽  
Vol 3 (3) ◽  
pp. 208-222
Author(s):  
B. Vivekanandam
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Models ◽  
Workplace Safety ◽  
Point Estimate ◽  
Light Weight ◽  
Neural Network Models ◽  
Crowd Counting ◽  
Wide Range ◽  
Trained Neural Network

In image/video analysis, crowds are actively researched, and their numbers are counted. In the last two decades, many crowd counting algorithms have been developed for a wide range of applications in crisis management systems, large-scale events, workplace safety, and other areas. The precision of neural network research for estimating points is outstanding in computer vision domain. However, the degree of uncertainty in the estimate is rarely indicated. Point estimate is beneficial for measuring uncertainty since it can improve the quality of decisions and predictions. The proposed framework integrates Light weight CNN (LW-CNN) for implementing crowd computing in any public place for delivering higher accuracy in counting. Further, the proposed framework has been trained through various scene analysis such as the full and partial vision of heads in counting. Based on the various scaling sets in the proposed neural network framework, it can easily categorize the partial vision of heads count and it is being counted accurately than other pre-trained neural network models. The proposed framework provides higher accuracy in estimating the headcounts in public places during COVID-19 by consuming less amount of time.

scholarly journals Using attribution to decode binding mechanism in neural network models for chemistry

2019 ◽  
pp. 201820657 ◽  
Author(s):  
Kevin McCloskey ◽  
Ankur Taly ◽  
Federico Monti ◽  
Michael P. Brenner ◽  
Lucy J. Colwell
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Specific Protein ◽  
Training Dataset ◽  
Binding Mechanism ◽  
Neural Network Models ◽  
Molecular Features ◽  
Drug Actions ◽  
Synthetic Datasets ◽  
Trained Neural Network

Deep neural networks have achieved state-of-the-art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from the networks could enable scientific discoveries about the mechanisms of drug actions. However, doing so requires shining light into the black box that is the trained neural network model, a task that has proved difficult across many domains. Here we show how the binding mechanism learned by deep neural network models can be interrogated, using a recently described attribution method. We first work with carefully constructed synthetic datasets, in which the molecular features responsible for “binding” are fully known. We find that networks that achieve perfect accuracy on held-out test datasets still learn spurious correlations, and we are able to exploit this nonrobustness to construct adversarial examples that fool the model. This makes these models unreliable for accurately revealing information about the mechanisms of protein–ligand binding. In light of our findings, we prescribe a test that checks whether a hypothesized mechanism can be learned. If the test fails, it indicates that the model must be simplified or regularized and/or that the training dataset requires augmentation.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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