scholarly journals Deep exploration networks for rapid engineering of functional DNA sequences

2019 ◽  
Author(s):  
Johannes Linder ◽  
Nicholas Bogard ◽  
Alexander B. Rosenberg ◽  
Georg Seelig
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Network Models ◽  
Regulatory Elements ◽  
Differential Splicing ◽  
Neural Network Models ◽  
Gradient Ascent ◽  
Functional Dna ◽  
The Cost ◽  
Deep Exploration

Engineering gene sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence generation. The generated sequences can however get stuck in local minima, have low diversity and their fitness depends heavily on initialization. Here, we develop deep exploration networks (DENs), a type of generative model tailor-made for searching a sequence space to minimize the cost of a neural network fitness predictor. By making the network compete with itself to control sequence diversity during training, we obtain generators capable of sampling hundreds of thousands of high-fitness sequences. We demonstrate the power of DENs in the context of engineering RNA isoforms, including polyadenylation and cell type-specific differential splicing. Using DENs, we engineered polyadenylation signals with more than 10-fold higher selection odds than the best gradient ascent-generated patterns and identified splice regulatory elements predicted to result in highly differential splicing between cell lines.

scholarly journals Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218073 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Genetic Variants ◽  
Network Models ◽  
Massively Parallel ◽  
Neural Network Models ◽  
Regulatory Dna Sequences ◽  
Reporter Assays ◽  
Regulatory Dna

A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

2018 ◽  
pp. 54-72
Author(s):  
C. M. Anish ◽  
Babita Majhi ◽  
Ritanjali Majhi
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Performance Measure ◽  
Neural Network Models ◽  
Proposed Model ◽  
Derivative Free ◽  
Asset Value ◽  
Net Asset Value ◽  
Small Investors ◽  
The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

2018 ◽  
pp. 1031-1049 ◽  
Author(s):  
C. M. Anish ◽  
Babita Majhi ◽  
Ritanjali Majhi
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Performance Measure ◽  
Neural Network Models ◽  
Proposed Model ◽  
Derivative Free ◽  
Asset Value ◽  
Net Asset Value ◽  
Small Investors ◽  
The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

scholarly journals Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

2018 ◽  
Vol 34 (17) ◽  
pp. i629-i637 ◽  
Author(s):  
Peyton Greenside ◽  
Tyler Shimko ◽  
Polly Fordyce ◽  
Anshul Kundaje
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Network Models ◽  
Neural Network Models ◽  
Feature Interactions ◽  
Regulatory Dna Sequences ◽  
Regulatory Dna

Estimation of the Temperatures in an Experimental Infrared Heated Greenhouse Using Neural Network Models

2013 ◽  
Vol 4 (2) ◽  
pp. 14-22 ◽  
Author(s):  
Angeliki Kavga ◽  
Vassilis Kappatos
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Network Models ◽  
Neural Network Models ◽  
High Quality ◽  
The Neural Network ◽  
The Cost ◽  
Greenhouse Control ◽  
Good Agreement ◽  
Indoor And Outdoor

A high quality greenhouse control demands continuous measurements of indoor and outdoor greenhouse conditions. In this work, neural network models were used for reducing the cost and the time of temperature measurements, estimating two of the most important parameters in the operation of the greenhouse, namely the inside air temperature and the cover temperature of greenhouse. An extensive experimental investigation was carried out in an infrared heated greenhouse, using the experimental data to train and validate the neural network models. The modelling results show that the estimated temperatures have been in good agreement with the experimental data with accuracy ranging from 95.64% to 97.67%.

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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