Classifying COVID-19 variants based on genetic sequences using deep learning models
Basu, S.; Campbell, R. H.
Show abstract
The COrona VIrus Disease (COVID-19) pandemic led to the occurrence of several variants with time. This has led to an increased importance of understanding sequence data related to COVID-19. In this chapter, we propose an alignment-free k-mer based LSTM (Long Short-Term Memory) deep learning model that can classify 20 different variants of COVID-19. We handle the class imbalance problem by sampling a fixed number of sequences for each class label. We handle the vanishing gradient problem in LSTMs arising from long sequences by dividing the sequence into fixed lengths and obtaining results on individual runs. Our results show that one-vs-all classifiers have test accuracies as high as 92.5% with tuned hyperparameters compared to the multi-class classifier model. Our experiments show higher overall accuracies for B.1.1.214, B.1.177.21, B.1.1.7, B.1.526, and P.1 on the one-vs-all classifiers, suggesting the presence of distinct mutations in these variants. Our results show that embedding vector size and batch sizes have insignificant improvement in accuracies, but changing from 2-mers to 3-mers mostly improves accuracies. We also studied individual runs which show that most accuracies improved after the 20th run, indicating that these sequence positions may have more contributions to distinguishing among different COVID-19 variants.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures 96%
- DeLUCS: Deep Learning for Unsupervised Clustering of DNA Sequences 95%
- Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study 94%
Similar papers in this journal
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 94%
- EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction 92%
- Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning. 92%
Similar papers in this journal
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 96%
- A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks 94%
- A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images 93%
Similar papers in this journal
- Multi-Head Attention-based U-Nets for Predicting Protein Domain Boundaries Using 1D Sequence Features and 2D Distance Maps 94%
- Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life 94%
- In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform 94%
Similar papers in this journal
- CRISPR-DIPOFF: An Interpretable Deep LearningApproach for CRISPR Cas-9 Off-Target Prediction 96%
- PRIEST - Predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information 95%
- Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.