Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses

Abstract

Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties of viral genomes such as recombination, mutation rate and diversity. Also, new generations of sequencing technologies rise other difficulties by generating massive amounts of fragmented sequences. While linear classifiers are often used to classify viruses, there is a lack of exploration of the accuracy space of existing models in the context of alignment free approaches. In this study, we present an exhaustive assessment procedure exploring the power of linear classifiers in genotyping and subtyping partial and complete genomes. It is applied to the Hepatitis C viruses (HCV). Several variables are considered in this investigation such as classifier types (generative and discriminative) and their hyper-parameters (smoothing value and regularization penalty function), the classification task (genotyping and subtyping), the length of the tested sequences (partial and complete) and the length of k-mer words. Overall, several classifiers perform well given a set of precise combination of the experimental variables mentioned above. Finally, we provide the procedure and benchmark data to allow for more robust assessment of classification from virus genomes.

Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification

“…The area under the curve for predicting N4-methylcytosine based on DNA sequences reached a significant value greater than 0.9 using the feature selection and stacking technique of a deep learning model [ 21 ]. The study in [ 22 ] investigated linear classifiers such as logistic regression, linear SVM, multinomial Bayes, Markov to identify the limited and whole genomic sequences of the HCV dataset. The authors tested and assessed the findings of a variety of K-mer sizes [ 22 ].…”

“…The study in [ 22 ] investigated linear classifiers such as logistic regression, linear SVM, multinomial Bayes, Markov to identify the limited and whole genomic sequences of the HCV dataset. The authors tested and assessed the findings of a variety of K-mer sizes [ 22 ]. Rincon et al [ 23 ] presented a technique for predicting SARS-CoV-2 with 100 percent specificity using a deep learning architecture.…”

Analysis of DNA Sequence Classification Using CNN and Hybrid Models

“…The linear classifier like Multinomial Bayes, Markov, Logistic Regression, and Linear SVM is used to classify the partial and complete genomic sequence of the HCV dataset is proposed. The author evaluated and compared the results for different K -mer size [ 15 ]. In [ 16 ], the author presented the method for predicting SARS-CoV-2 using the deep learning architecture with 100% specificity.…”