Classifying COVID-19 variants based on genetic sequences using deep learning models

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.

Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences

“…For example, we can extract k -mer (short subsequence) frequencies or other combinations of bases/amino acids and use them as features to train classifiers using naive Bayes classifier (NBC), support vector machines (SVM), decision tree-based methods, and neural networks ( 43 – 49 ). Machine learning with k -mer features has been used for SARS-CoV-2 to identify genetic fingerprints of specific infections ( 50 ), classify variants ( 51 , 52 ), and train a model to predict the pathogenicity of unknown viruses ( 53 ). Another approach is to build profile hidden Markov models (HMMs), which can identify taxonomic lineages and variants of viruses.…”

Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants

“…Many models are developed to predict a possible infection of patients only by the reported symptoms, as the review of Huang et al shows ( 3 ). For example, Manni et al ( 4 ) developed a logistic regression to distinguish between index and contact persons and Drew et al ( 5 ) published an app which revealed symptom combinations which are predictive for a positive COVID-19 test. Spinato et al ( 6 ) created and validated a differentiated questionnaire to identify index cases based on symptomatology.…”

Interpretable and Predictive Deep Modeling of the SARS-CoV-2 Spike Protein Sequence

“…[82] Another work applied Long-Short Term Memory RNNs (LSTM) to classify sequences to lineages. [83]…”

“…[82] Another work applied Long-Short Term Memory RNNs (LSTM) to classify sequences to lineages. [83] While deep learning methods can identify complex features within data that allow classification, that strength comes with a major weakness: understanding what the deep model focused on in learning the classification and explaining its predictions. Accordingly, interpretable machine learning has emerged as a significant area of research.…”