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Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses
- Source :
- BIBM
- Publication Year :
- 2019
- Publisher :
- arXiv, 2019.
-
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.<br />Comment: Accepted as a regular paper for publication in IEEE BIBM 2019 (Camera-ready version + Supplemental material)
- Subjects :
- Genomics (q-bio.GN)
FOS: Computer and information sciences
0303 health sciences
Mutation rate
Computer Science - Machine Learning
Computer science
0206 medical engineering
Linear model
Machine Learning (stat.ML)
02 engineering and technology
Computational biology
Genome
Subtyping
3. Good health
Machine Learning (cs.LG)
03 medical and health sciences
Discriminative model
Statistics - Machine Learning
FOS: Biological sciences
Quantitative Biology - Genomics
Genotyping
Classifier (UML)
020602 bioinformatics
Smoothing
030304 developmental biology
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- BIBM
- Accession number :
- edsair.doi.dedup.....ee5dc7120c26a9c6855b2611d74b2f6c
- Full Text :
- https://doi.org/10.48550/arxiv.1910.05421