Species annotation using a k-mer based KNN model

Bacterial identification is a critical process in microbiology, clinical diagnostics, environmental monitoring, and food safety. Machine learning holds great promise for improving bacterial identification by increasing accuracy, speed, and scalability. However, challenges such as data dependency, mo...

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Bibliographic Details
Published in:Bioinformation 2024-09, Vol.20 (9), p.986
Main Authors: Sangar, Srushti, Kolage, Prathamesh, Chunarkar-Patil, Pritee
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Annotations
Bacteria
Environmental monitoring
Food safety
Frequency distribution
Machine learning
Microbiology
Online Access:Get full text
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Summary:Bacterial identification is a critical process in microbiology, clinical diagnostics, environmental monitoring, and food safety. Machine learning holds great promise for improving bacterial identification by increasing accuracy, speed, and scalability. However, challenges such as data dependency, model interpretability, and computational demands must be addressed to fully realize it's potential. k-mer based bacterial identification algorithm is an attempt to address these issues. Sequence matching is completed using the KNN technique. This included feature extraction, dataset preparation, classifier training, and label prediction based on k-mer frequency distribution similarity. The algorithm's performance has been cross-checked through accuracy assessment metrics such as F1 score and precision with an impressive 93% accuracy rate.
ISSN:0973-8894
0973-2063
DOI:10.6026/973206300200986