scholarly journals RDense: A Protein-RNA Binding Prediction Model Based on Bidirectional Recurrent Neural Network and Densely Connected Convolutional Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 14588-14605 ◽  
Author(s):  
Zhong Li ◽  
Jiapeng Zhu ◽  
Xiaojiang Xu ◽  
Yuhua Yao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Rna Binding ◽  
Binding Prediction ◽  
Convolutional Networks ◽  
Model Based

Approach for Learning Intention Prediction Model based on Recurrent Neural Network

2018 ◽  
Vol 45 (4) ◽  
pp. 360-369 ◽  
Author(s):  
Sung-hyuk Bang ◽  
Seok-Hyun Bae ◽  
Hyun-Kyu Park ◽  
Myung-Joong Jeon ◽  
Je-Min Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Model Based ◽  
Learning Intention

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

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