Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences
OPEN ACCESS
Loading...
Author / Producer
Date
2021-06
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
12
Pages / Article No.
644487
Publisher
Frontiers Media
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Microbiome; Metagenomics; Marker-gene sequencing; Taxonomic classification; Machine learning; Neural networks
Organisational unit
09714 - Bokulich, Nicholas / Bokulich, Nicholas