A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences
In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Comm...
Saved in:
Main Authors: | Mohanad A. Deif, Ahmed A. A. Solyman, Mehrdad Ahmadi Kamarposhti, Shahab S. Band, Rania E. Hammam |
---|---|
Format: | article |
Language: | EN |
Published: |
AIMS Press
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/e55ce32d10b740d4a30e6d7e3bf5a80c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
by: Balduíno César Mateus, et al.
Published: (2021) -
Bangla hate speech detection on social media using attention-based recurrent neural network
by: Das Amit Kumar, et al.
Published: (2021) -
Universal gated recurrent unit-based 3D localization method for ultra-wideband systems
by: Doan Tan Anh Nguyen, et al.
Published: (2021) -
Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
by: Shawni Dutta, et al.
Published: (2020) -
Analysis of Gradient Vanishing of RNNs and Performance Comparison
by: Seol-Hyun Noh
Published: (2021)