Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

2021 ◽  
Author(s):  
Jun Liao ◽  
Dandan Liu ◽  
Guoxin Su ◽  
Li Liu
Keyword(s):  
Physiological Signals ◽  
Lstm Network

Emotion Monitoring Using Remote Measurement for Physiological Signals by Camera

2016 ◽  
Vol 2016 (1) ◽  
pp. 147-151 ◽  
Author(s):  
Genki Okada ◽  
Taku Yonezawa ◽  
Kouki Kurita ◽  
Norimichi Tsumura
Keyword(s):  
Physiological Signals ◽  
Remote Measurement

An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Liwei Huang ◽  
Yutao Ma ◽  
Shibo Wang ◽  
Yanbo Liu
Keyword(s):  
Poi Recommendation ◽  
Lstm Network

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

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