دورية أكاديمية
Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences.
| العنوان: | Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences. |
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| المؤلفون: | Ziemski M; Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition, and Health, ETH Zürich, Zurich, Switzerland., Wisanwanichthan T; School of Science, University of New South Wales, Canberra, ACT, Australia., Bokulich NA; Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition, and Health, ETH Zürich, Zurich, Switzerland., Kaehler BD; School of Science, University of New South Wales, Canberra, ACT, Australia. |
| المصدر: | Frontiers in microbiology [Front Microbiol] 2021 Jun 18; Vol. 12, pp. 644487. Date of Electronic Publication: 2021 Jun 18 (Print Publication: 2021). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101548977 Publication Model: eCollection Cited Medium: Print ISSN: 1664-302X (Print) Linking ISSN: 1664302X NLM ISO Abbreviation: Front Microbiol Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Lausanne : Frontiers Research Foundation |
| مستخلص: | Naive Bayes classifiers (NBC) have dominated the field of taxonomic classification of amplicon sequences for over a decade. Apart from having runtime requirements that allow them to be trained and used on modest laptops, they have persistently provided class-topping classification accuracy. In this work we compare NBC with random forest classifiers, neural network classifiers, and a perfect classifier that can only fail when different species have identical sequences, and find that in some practical scenarios there is little scope for improving on NBC for taxonomic classification of 16S rRNA gene sequences. Further improvements in taxonomy classification are unlikely to come from novel algorithms alone, and will need to leverage other technological innovations, such as ecological frequency information. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Ziemski, Wisanwanichthan, Bokulich and Kaehler.) |
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| فهرسة مساهمة: | Keywords: machine learning; marker-gene sequencing; metagenomics; microbiome; neural networks; taxonomic classification |
| تواريخ الأحداث: | Date Created: 20210705 Latest Revision: 20240402 |
| رمز التحديث: | 20240402 |
| مُعرف محوري في PubMed: | PMC8249850 |
| DOI: | 10.3389/fmicb.2021.644487 |
| PMID: | 34220738 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1664-302X |
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| DOI: | 10.3389/fmicb.2021.644487 |