A recurrent neural network model for biomass gasification chemistry

2021 ◽  
Author(s):  
Krishna Gopal Sharma ◽  
Niket S. Kaisare ◽  
Himanshu Goyal
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Kinetic Models ◽  
Computational Cost ◽  
Biomass Gasification ◽  
Chemical Kinetic ◽  
Thermochemical Conversion ◽  
High Computational Cost ◽  
Detailed Chemical

Detailed chemical kinetic models involving hundreds of species and thousands of reactions have recently been developed for biomass thermochemical conversion. The high computational cost of these kinetic models makes them...

scholarly journals Prospects for recurrent neural network models to learn RNA biophysics from high-throughput data

2017 ◽  
Author(s):  
Michelle J Wu ◽  
Johan OL Andreasson ◽  
Wipapat Kladwang ◽  
William J Greenleaf ◽  
Rhiju Das ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Free Energy ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Rna Folding ◽  
Large Datasets ◽  
Turner Model ◽  
Rna Complexes

AbstractRNA is a functionally versatile molecule that plays key roles in genetic regulation and in emerging technologies to control biological processes. Computational models of RNA secondary structure are well-developed but often fall short in making quantitative predictions of the behavior of multi-RNA complexes. Recently, large datasets characterizing hundreds of thousands of individual RNA complexes have emerged as rich sources of information about RNA energetics. Meanwhile, advances in machine learning have enabled the training of complex neural networks from large datasets. Here, we assess whether a recurrent neural network model, Ribonet, can learn from high-throughput binding data, using simulation and experimental studies to test model accuracy but also determine if they learned meaningful information about the biophysics of RNA folding. We began by evaluating the model on energetic values predicted by the Turner model to assess whether the neural network could learn a representation that recovered known biophysical principles. First, we trained Ribonet to predict the simulated free energy of an RNA in complex with multiple input RNAs. Our model accurately predicts free energies of new sequences but also shows evidence of having learned base pairing information, as assessed by in silico double mutant analysis. Next, we extended this model to predict the simulated affinity between an arbitrary RNA sequence and a reporter RNA. While these more indirect measurements precluded the learning of basic principles of RNA biophysics, the resulting model achieved sub-kcal/mol accuracy and enabled design of simple RNA input responsive riboswitches with high activation ratios predicted by the Turner model from which the training data were generated. Finally, we compiled and trained on an experimental dataset comprising over 600,000 experimental affinity measurements published on the Eterna open laboratory. Though our tests revealed that the model likely did not learn a physically realistic representation of RNA interactions, it nevertheless achieved good performance of 0.76 kcal/mol on test sets with the application of transfer learning and novel sequence-specific data augmentation strategies. These results suggest that recurrent neural network architectures, despite being naïve to the physics of RNA folding, have the potential to capture complex biophysical information. However, more diverse datasets, ideally involving more direct free energy measurements, may be necessary to train de novo predictive models that are consistent with the fundamentals of RNA biophysics.Author SummaryThe precise design of RNA interactions is essential to gaining greater control over RNA-based biotechnology tools, including designer riboswitches and CRISPR-Cas9 gene editing. However, the classic model for energetics governing these interactions fails to quantitatively predict the behavior of RNA molecules. We developed a recurrent neural network model, Ribonet, to quantitatively predict these values from sequence alone. Using simulated data, we show that this model is able to learn simple base pairing rules, despite having no a priori knowledge about RNA folding encoded in the network architecture. This model also enables design of new switching RNAs that are predicted to be effective by the “ground truth” simulated model. We applied transfer learning to retrain Ribonet using hundreds of thousands of RNA-RNA affinity measurements and demonstrate simple data augmentation techniques that improve model performance. At the same time, data diversity currently available set limits on Ribonet’s accuracy. Recurrent neural networks are a promising tool for modeling nucleic acid biophysics and may enable design of complex RNAs for novel applications.

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

2021 ◽  
Vol 193 (12) ◽  
Author(s):  
Salar Valizadeh Moghadam ◽  
Ahmad Sharafati ◽  
Hajar Feizi ◽  
Seyed Mohammad Saeid Marjaie ◽  
Seyed Babak Haji Seyed Asadollah ◽  
...  
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Oxygen Concentration ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Dissolved Oxygen Concentration ◽  
Efficient Strategy ◽  
Deep Recurrent Neural Network

Dynamics of Vertebral Column Observed by Stereovision and Recurrent Neural Network Model

2005 ◽  
pp. 51-60
Author(s):  
C. Fernando Mugarra Gonzalez ◽  
Stanisław Jankowski ◽  
Jacek J. Dusza ◽  
Vicente Carrilero López ◽  
Javier M. Duart Clemente
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Vertebral Column
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