scholarly journals Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes

Biomolecules ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 67 ◽  
Author(s):  
Óscar Álvarez-Machancoses ◽  
Enrique J. De Andrés-Galiana ◽  
Juan Luis Fernández-Martínez ◽  
Andrzej Kloczkowski
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Network Model ◽  
Neural Network Model ◽  
Pearson Correlation ◽  
Single Point ◽  
Point Mutations ◽  
Cumulative Distribution ◽  
Multiple Point ◽  
Protein Mutations

Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coefficients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field.

scholarly journals Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Animals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 364
Author(s):  
Song Zhang ◽  
Xinting Yang ◽  
Yizhong Wang ◽  
Zhenxi Zhao ◽  
Jintao Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Production Management ◽  
Pearson Correlation ◽  
Ground Truth ◽  
Fish Population ◽  
Convolution Neural Network ◽  
Hybrid Neural Network ◽  
Structure Information

In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

scholarly journals Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Canyang Guo ◽  
Genggeng Liu ◽  
Chi-Hua Chen
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Rapid Development ◽  
Pearson Correlation ◽  
Stochastic Gradient Descent ◽  
Prediction Methods ◽  
Pollution Concentration

The global environment has become more polluted due to the rapid development of industrial technology. However, the existing machine learning prediction methods of air quality fail to analyze the reasons for the change of air pollution concentration because most of the prediction methods take more focus on the model selection. Since the framework of recent deep learning is very flexible, the model may be deep and complex in order to fit the dataset. Therefore, overfitting problems may exist in a single deep neural network model when the number of weights in the deep neural network model is large. Besides, the learning rate of stochastic gradient descent (SGD) treats all parameters equally, resulting in local optimal solution. In this paper, the Pearson correlation coefficient is used to analyze the inherent correlation of PM2.5 and other auxiliary data such as meteorological data, season data, and time stamp data which are applied to cluster for enhancing the performance. Extracted features are helpful to build a deep ensemble network (EN) model which combines the recurrent neural network (RNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) network to predict the PM2.5 concentration of the next hour. The weights of the submodel change with the accuracy of them in the validation set, so the ensemble has generalization ability. The adaptive moment estimation (Adam) an algorithm for stochastic optimization is used to optimize the weights instead of SGD. In order to compare the overall performance of different algorithms, the mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as accuracy metrics in the experiments of this study. The experiment results show that the proposed method achieves an accuracy rate (i.e., MAE=6.19 and MAPE=16.20%) and outperforms the comparative models.

scholarly journals A neural network model for the prediction of membrane-spanning amino acid sequences

1994 ◽  
Vol 3 (9) ◽  
pp. 1597-1601 ◽  
Author(s):  
Reinhard Lohmann ◽  
Gisbert Schneider ◽  
Dirk Behrens ◽  
Paul Wrede
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Network Model ◽  
Neural Network Model ◽  
Amino Acid Sequences ◽  
Membrane Spanning
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