scholarly journals Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

2021 ◽  
Author(s):  
Chen Chen ◽  
Tianqi Wu ◽  
Zhiye Guo ◽  
Jianlin Cheng
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Contact Prediction

Deep Neural Network Guided by Attention Mechanism for Segmentation of Liver Pathology Image

2021 ◽  
pp. 425-435
Author(s):  
Zhongrui Zhai ◽  
Chaoli Wang ◽  
Zhanquan Sun ◽  
Shuqun Cheng ◽  
Kang Wang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Liver Pathology ◽  
Pathology Image

scholarly journals Deep Neural Network for Protein Contact Prediction by Weighting Sequences in a Multiple Sequence Alignment

2018 ◽  
Author(s):  
Hiroyuki Fukuda ◽  
Kentaro Tomii
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structure Prediction ◽  
Deep Neural Network ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Meta Learning ◽  
Correlation Information

AbstractProtein contact prediction is a crucially important step for protein structure prediction. To predict a contact, approaches of two types are used: evolutionary coupling analysis (ECA) and supervised learning. ECA uses a large multiple sequence alignment (MSA) of homologue sequences and extract correlation information between residues. Supervised learning uses ECA analysis results as input features and can produce higher accuracy. As described herein, we present a new approach to contact prediction which can both extract correlation information and predict contacts in a supervised manner directly from MSA using a deep neural network (DNN). Using DNN, we can obtain higher accuracy than with earlier ECA methods. Simultaneously, we can weight each sequence in MSA to eliminate noise sequences automatically in a supervised way. It is expected that the combination of our method and other meta-learning methods can provide much higher accuracy of contact prediction.

scholarly journals Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1827
Author(s):  
Dengao Li ◽  
Hang Wu ◽  
Jumin Zhao ◽  
Ye Tao ◽  
Jian Fu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Public Database ◽  
Ecg Signals ◽  
The World ◽  
Neural Network Structure ◽  
Test Sets ◽  
Gated Recurrent Units

Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.

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