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Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA
Marika Kaden1,2, Katrin Sophie Bohnsack1,2, Mirko Weber1,2
1University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.
Neural Computing & Applications
|May 3, 2021
Summary
This study introduces a novel method for classifying SARS-CoV-2 virus types using RNA sequence features and prototype-based classification, offering an interpretable alternative to sequence alignment for identifying viral variants.
Area of Science:
- Genomics
- Bioinformatics
- Virology
Background:
- Accurate and efficient classification of SARS-CoV-2 variants is crucial for public health surveillance.
- Traditional sequence alignment methods can be computationally intensive.
Purpose of the Study:
- To develop an interpretable, alignment-free method for discriminating SARS-CoV-2 virus types.
- To identify potential novel SARS-CoV-2 variants through analysis of rejected sequences.
Main Methods:
- Feature extraction from RNA sequences to create feature vectors.
- Application of prototype-based classification, specifically Learning Vector Quantization (LVQ) variants.
- Incorporation of reject options for uncertain classifications.
Main Results:
- Successful classification of SARS-CoV-2 types on a GISAID dataset.
- Identification of atypical sequences that may represent novel variants.
- Demonstrated lower computational complexity compared to sequence alignment methods.
Conclusions:
- The proposed LVQ-based approach provides an efficient and interpretable method for SARS-CoV-2 variant classification.
- The ability to reject uncertain samples aids in discovering new viral mutations and improves model robustness.
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