ASFold-DNN: Protein Fold Recognition based on Evolutionary Features with Variable Parameters using Full Connected Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Xinyi Qin ◽  
Lu Zhang ◽  
in Liu ◽  
Ziwei Xu ◽  
Guangzhong Liu
Keyword(s):  
Neural Network ◽  
Fold Recognition ◽  
Protein Fold ◽  
Variable Parameters ◽  
Protein Fold Recognition ◽  
Evolutionary Features

scholarly journals A novel feature fusion based on the evolutionary features for protein fold recognition using support vector machines

2019 ◽  
Author(s):  
Mohammad Saleh Refahi ◽  
A. Mir ◽  
Jalal A. Nasiri
Keyword(s):  
Feature Extraction ◽  
Information Gain ◽  
Fold Recognition ◽  
Dimensional Structure ◽  
Support Vector ◽  
Protein Fold ◽  
Protein Fold Recognition ◽  
Evolutionary Features ◽  
Cross Covariance ◽  
Benchmark Datasets

AbstractProtein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physiochemical-based information to extract features. In recent years, Finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance (ACC) and Separated dimer (SD) evolutionary feature extraction methods. The results features are scored by Information gain (IG) to define and select several discriminated features. According to three benchmark datasets, DD, RDD and EDD, the results of the support vector machine (SVM) show more than 6% improvement in accuracy on these benchmark datasets.

scholarly journals DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Wessam Elhefnawy ◽  
Min Li ◽  
Jianxin Wang ◽  
Yaohang Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Fold Recognition ◽  
Major Protein ◽  
Learning Approach ◽  
Protein Fold ◽  
Protein Fold Recognition ◽  
Second Stage ◽  
Two Stages

Abstract Background One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. Conclusions There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.

scholarly journals Protein fold recognition using geometric kernel data fusion

2014 ◽  
Vol 30 (13) ◽  
pp. 1850-1857 ◽  
Author(s):  
Pooya Zakeri ◽  
Ben Jeuris ◽  
Raf Vandebril ◽  
Yves Moreau
Keyword(s):  
Data Fusion ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition
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