An Integrated Approach for Protein Structure Prediction Using Artificial Neural Network

2010 ◽  
Author(s):  
Hassan Mathkour ◽  
Muneer Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Integrated Approach ◽  
Artificial Neural

Protein structure prediction based on BN-GRU method

2020 ◽  
Vol 18 (06) ◽  
pp. 2050045
Author(s):  
Lina Yang ◽  
Pu Wei ◽  
Cheng Zhong ◽  
Xichun Li ◽  
Yuan Yan Tang
Keyword(s):  
Neural Network ◽  
Protein Structure ◽  
Spatial Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Main Idea ◽  
Traditional Methods ◽  
Analysis Of Similarity ◽  
New Feature ◽  
Activity Mechanism

The spatial structure of the protein reflects the biological function and activity mechanism. Predicting the secondary structure of a protein is the basis content for predicting its spatial structure. Traditional methods based on statistics and sequential patterns do not achieve higher accuracy. In this paper, the application of BN-GRU neural network in protein structure prediction is discussed. The main idea is to construct a Gated Recurrent Unit (GRU) neural network. The GRU neural network can learn long-term dependencies. It can handle long sequences better than traditional methods. Based on this, BN is combined with GRU to construct a new network. Position Specific Scoring Matrix (PSSM) is used to associate with other features to build a completely new feature set. It can be proved that the application of BN on GRU can improve the accuracy of the results. The idea in this paper can also be applied to the analysis of similarity of other sequences.

An optimal design of axial-flow fan blades by the machining method and an artificial neural network

2002 ◽  
Vol 216 (3) ◽  
pp. 367-376 ◽  
Author(s):  
B-J Lin ◽  
C-I Hung ◽  
E-J Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tool Path ◽  
Three Dimensional ◽  
Axial Flow ◽  
Integrated Approach ◽  
Optimization Method ◽  
Surface Geometry ◽  
Axial Flow Fan ◽  
Artificial Neural

The geometry design and machining of blades for axial-flow fans are important issues because the twisted profile and flowfield of blades are complicated. The rapid design of a blade that performs well and satisfies machining requirements is one of the goals in designing fluid machinery blades. In this study, an integrated approach combining computational fluid dynamics (CFD), an artificial neural network, an optimization method and a machining method is proposed to design a three-dimensional blade for an axial-flow fan. From the machining point of view, the three-dimensional surface geometry of a fan blade can be defined as the swept surface of the tool path created by using the generated machining method. By taking advantage of its powerful learning capability, a back-propagation artificial neural network is used to set up the flowfield models and to forecast the flow performance of the axial-flow fan. The desired optimal blade geometry is obtained by using a complex optimization method.

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