Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence
Not on the map. Why?
Abstract (arXiv)
SARS-CoV-2 is an upper respiratory system RNA virus that has caused over 3 million deaths and infecting over 150 million worldwide as of May 2021. With thousands of strains sequenced to date, SARS-CoV-2 mutations pose significant challenges to scientists on keeping pace with vaccine development and public health measures. Therefore, an efficient method of identifying the divergence of lab samples from patients would greatly aid the documentation of SARS-CoV-2 genomics. In this study, we propose a neural network model that leverages recurrent and convolutional units to directly take in amino acid sequences of spike proteins and classify corresponding clades. We also compared our model's performance with Bidirectional Encoder Representations from Transformers (BERT) pre-trained on protein database. Our approach has the potential of providing a more computationally efficient alternative to current homology based intra-species differentiation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 12 Nov 2021.
Builds on
6 of 27 references.
References · 6
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
- 2019ProteinNet: a standardized data set for machine learning of protein structurecited by 39
- 2016Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Datacited by 17
- 2017Deep Recurrent Neural Network for Protein Function Prediction from Sequencecited by 8
Built on by
Not cited yet.