scholarly journals Protein homodimers structure prediction based on deep neural network

Informatics ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 44-53
Author(s):  
A. Y. Hadarovich ◽  
A. A. Kalinouski ◽  
A. V. Tuzikov
Keyword(s):  
Neural Network ◽  
Protein Complex ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Optimization Procedure ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Contact Map ◽  
Suggested Approach

Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimental techniques to identify the structure of protein complex require enormous amount of time and resources, and each method has its own limitations. Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. Building on the idea of this approach, we developed an algorithm to model the 3D structure of homodimer based on deep learning. It consists of two major steps: at the first step a protein complex contact map is predicted with the deep convolutional neural network, and the second stage is used to predict 3D structure of homodimer based on obtained contact map and optimization procedure. The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. The suggested approach was tested and validated on a dataset of protein homodimers from Protein Data Bank (PDB). The developed procedure could be also used for evaluating protein homodimer models as one of the stages in drug compounds developing.

scholarly journals Assess Reliability Parameters of an Electronic Voting Machine using a Neural Network Technique

2020 ◽  
Vol 9 (6) ◽  
pp. 269-275
Keyword(s):  
Neural Network ◽  
Storage System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Descent Method ◽  
Electronic Voting ◽  
Gradient Descent Method ◽  
Neural Network Approach ◽  
Reliability Parameters ◽  
Voting Machine

The structure of Electronic Voting Machine (EVM) is an interconnected network of discrete components that record and count the votes of voters. The EVM system consists of four main subsystems which are Mother board of computer, Voting keys, Database storage system, power supply (AC and DC) along with various conditions of functioning as well as deficiency. The deficiency or failure of system is due to its components (hardware), software and human mismanagement. It is essential to reduce complexity of interconnected components and increase system reliability. Reliability analysis helps to identify technical situations that may affect the system and to predict the life of the system in future. The aim of this research paper is to analyze the reliability parameters of an EVM system using one of the approaches of computational intelligence, the neural network (NN). The probabilistic equations of system states and other reliability parameters are established for the proposed EVM model using neural network approach. It is useful for predicting various reliability parameters and improves the accuracy and consistency of parameters. To guarantee the reliability of the system, Back Propagation Neural Network (BPNN) architecture is used to learn a mechanism that can update the weights which produce optimal parameters values. Numerical examples are considered to authenticate the results of reliability, unreliability and profit function. To minimize the error and optimize the output in the form of reliability using gradient descent method, authors iterate repeatedly till the precision of 0.0001 error using MATLAB code. These parameters are of immense help in real time applications of Electronic Voting Machine during elections.

scholarly journals Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 432 ◽  
Author(s):  
Chandran Nithin ◽  
Pritha Ghosh ◽  
Janusz Bujnicki
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Structural Models ◽  
Computational Prediction ◽  
Computational Docking ◽  
3D Structure Prediction ◽  
Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

scholarly journals Adaptive Fuzzy / PD controller for Coupled Two Tank Liquid Levels system

2018 ◽  
Vol 225 ◽  
pp. 06020
Author(s):  
Ahmed N Abdalla ◽  
Thamir K Ibrahim ◽  
Hai Tao
Keyword(s):  
Neural Network ◽  
Spray Coating ◽  
Adaptive Controller ◽  
Descent Method ◽  
Fine Tuning ◽  
Effluent Treatment ◽  
Control Technique ◽  
Response Analysis ◽  
Gradient Descent Method ◽  
Fuzzy Adaptive

Liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. A typical situation is one that requires fluid to be supplied to a tank at a constant rate which has Two-Input Two-Output (TITO). The accuracy and stability of TITO is one of the key factors of process which have cross coupling between process input and output. Unlike traditional neural network weight adaptation using gradient descent method, Fuzzy Adaptive PID control technique was utilized for adaptive tuning and fine tuning the controller’s parameters. Design approach for controlling liquid levels of Coupled Tank TITO system by using Fuzzy Adaptive PID controllers. Tuning method for parameters of improved Fuzzy Adaptive, Particle Swamp Optimization and Neural network controllers ware also discussed. Experimental results demonstrate that the performances of proposed method perform well compared with convention Fuzzy and Tuned Fuzzy controllers; it was shown that Fuzzy Adaptive controller exhibited better performance in terms of transient response analysis.

scholarly journals 3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction

2019 ◽  
Vol 20 (17) ◽  
pp. 4116 ◽  
Author(s):  
Jun Wang ◽  
Jian Wang ◽  
Yanzhao Huang ◽  
Yi Xiao
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Web Server ◽  
Structure Optimization ◽  
Optimization Procedure ◽  
Biological Functions ◽  
Rna Sequences ◽  
3D Structures ◽  
3D Structure Prediction ◽  
Sampling Structure

3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited in comparison with known RNA sequences up to now. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. In recent years, 3dRNA has been greatly improved by adding several important features, including structure sampling, structure ranking and structure optimization under residue-residue restraints. Particularly, the optimization procedure with restraints enables 3dRNA to treat pseudoknots in a new way. These new features of 3dRNA can greatly promote its performance and have been integrated into the 3dRNA v2.0 web server. Here we introduce these new features in the 3dRNA v2.0 web server for the users.

scholarly journals Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jianyong Liu ◽  
Huaixiao Wang ◽  
Yangyang Sun ◽  
Chengqun Fu ◽  
Jie Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Search Space ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Quantum Genetic Algorithm ◽  
Binary Coding ◽  
Optimal Value ◽  
Neural Network Algorithm

The method that the real-coded quantum-inspired genetic algorithm (RQGA) used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA) is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

scholarly journals Prediction of 8-state protein secondary structures by 1D-Inception and BD-LSTM

2019 ◽  
Author(s):  
Aminur Rab Ratul ◽  
Marcel Turcotte ◽  
M. Hamed Mozaffari ◽  
WonSook Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Research Area ◽  
Protein Secondary Structures ◽  
Precise Prediction

AbstractProtein secondary structure is crucial to create an information bridge between the primary structure and the tertiary (3D) structure. Precise prediction of 8-state protein secondary structure (PSS) significantly utilized in the structural and functional analysis of proteins in bioinformatics. In this recent period, deep learning techniques have been applied in this research area and raise the Q8 accuracy remarkably. Nevertheless, from a theoretical standpoint, there still lots of room for improvement, specifically in 8-state (Q8) protein secondary structure prediction. In this paper, we presented two deep learning architecture, namely 1D-Inception and BD-LSTM, to improve the performance of 8-classes PSS prediction. The input of these two architectures is a carefully constructed feature matrix from the sequence features and profile features of the proteins. Firstly, 1D-Inception is a Deep convolutional neural network-based approach that was inspired by the InceptionV3 model and containing three inception modules. Secondly, BD-LSTM is a recurrent neural network model which including bidirectional LSTM layers. Our proposed 1D-Inception method achieved 76.65%, 71.18%, 76.86%, and 74.07% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Moreover, BD-LSTM acquired 74.71%, 69.49%, 74.07%, and 72.37% state-8 accuracy after evaluated on CullPdb6133, CB513, CASP10, and CASP11 datasets, respectively. Both these architectures enable the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class is very beneficial in the deep neural network. To the best of our knowledge, experiment results of the 1D-Inception model demonstrate that it outperformed all the state-of-art methods on the benchmark CullPdb6133, CB513, and CASP10 datasets.

Fault diagnosis method of mine hoist braking system based on deep convolution neural network

2021 ◽  
Author(s):  
Fangyuan Yan ◽  
Juanli Li ◽  
Dong Miao ◽  
Qi Cao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Descent Method ◽  
Convolution Neural Network ◽  
Monitoring Data ◽  
Gradient Descent Method ◽  
Braking System ◽  
Diagnosis Method ◽  
Mine Hoist

Abstract A reliable braking system is an important guarantee for safe operation of mine hoist. In order to make full use of the monitoring data in the operation process of mine hoist, identify the operation status of the hoist, and further carry out fault diagnosis on it, the deep learning method was introduced into the fault diagnosis of the hoist, and a fault diagnosis method of hoist braking system based on convolution neural network has been proposed. Firstly, the working principle and fault mechanism of disc brake and its hydraulic station in hoist braking system are analyzed, and the monitoring parameters of this study are determined; then, based on massive monitoring data, the convolutional neural networks (CNN) is established, the one-dimensional signal collected by the sensor is transformed into two-dimensional image for coding, the neural network is trained by gradient descent method, and the network structure parameters are modified according to the training results. Finally, the fault diagnosis model is compared and verified by using the sample set based on the traditional back propagation neural network (BP) and CNN. The results show that the accuracy of CNN is higher than that of BP, and the accuracy rate can reach 99.375% after reducing the involvement between samples. This method can make full use of the monitoring data for diagnosis, without subjective intervention of experts, and improve the accuracy of diagnosis.

Generalized Predictive Control of Neural Network Based on LM Optimization

2011 ◽  
Vol 66-68 ◽  
pp. 2164-2169
Author(s):  
Si Ming Wang ◽  
Jian Yu Xu ◽  
Yu Qian Zhai
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Function ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Matlab Simulation ◽  
Bp Network ◽  
The Neural Network ◽  
Fast Convergence Rate ◽  
Lm Algorithm

Starting from the establishment of generalized predictive model based on neural network, LM optimization algorithm is applied to the perdictive control model for study in order to solve these problems that the training speed of the BP network is slow and it is easy to trap into the local minimum.Generalized predictive control and neural network which has the capability of approaching any nonlinear function are combined to forecast the future outputs of the system.LM algorithm is used instead of gradient descent method to optimize controller parameters and it makes full use of Jacobian matrix information identified by neural network.The result of Matlab simulation indicates that the neural network using LM algorithm has the feature of fast convergence rate,model of high precision and good robustness,which is more suitable for real-time nonlinear control.

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