scholarly journals Improving Protein Fold Recognition by Deep Learning Networks

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Taeho Jo ◽  
Jie Hou ◽  
Jesse Eickholt ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Fold Recognition ◽  
Protein Fold ◽  
Learning Networks ◽  
Protein Fold Recognition

DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks

2019 ◽  
Vol 21 (5) ◽  
pp. 1733-1741 ◽  
Author(s):  
Bin Liu ◽  
Chen-Chen Li ◽  
Ke Yan
Keyword(s):  
Deep Learning ◽  
Sequence Similarity ◽  
Fold Recognition ◽  
Support Vector ◽  
Protein Fold ◽  
Learning Networks ◽  
Protein Fold Recognition ◽  
Pairwise Sequence Similarity ◽  
New Feature ◽  
Similarity Scores

Abstract Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods.

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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