Classifying the bacterial taxonomy with its metagenomic data using the deep neural network model

Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genom...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2023-11, Vol.45 (5), p.7603-7618
Main Authors: Raman, Ramakrishnan, Barve, Amit, Meenakshi, R., Jayaseelan, G.M., Ganeshan, P., Taqui, Syed Noeman, Almoallim, Hesham S., Alharbi, Sulaiman Ali, Raghavan, S.S.
Format: Article
Language:English
Subjects:
Artificial neural networks
Bacteria
Belief networks
Classification
Classifiers
Deep learning
Feature extraction
Machine learning
Neural networks
Representations
Sequences
Taxonomy
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Summary:Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genomes were used to generate either SG or AMP short-reads. Then, to map sequencing as matrices into such a number space, used a k-mer model. Our analysis of the existing approaches revealed several drawbacks, including limited ability to handle complex hierarchical representations of data and suboptimal feature extraction from grid-like structures. To overcome these limitations, we introduce DBNs for feature learning and dimensionality reduction, and CNNs for efficient processing of grid-like metagenomic data. Finally, a training set for every taxon was obtained by training two distinct deep learning constructions, specifically deep belief network (DBN) and convolutional neural network (CNN). This examined the proposed methodology to determine the best factor that determines and compared findings to the classification abilities offered by the RDP classifier, a standard classifier for bacterium identification. These designs outperform using RDP classifiers at every taxonomic level. So, at the genetic level, for example, both CNN and DBN achieved 91.4% accuracy using AMP short-reads, but the RDP classifier achieved 83.9% with the same information. This paper, suggested a classification method for 16 S short-read sequences created on k-mer representations and a deep learning structure, that every taxon creates a classification method. The experimental findings validate the suggested pipelines as a realistic strategy for classifying bacterium samples; as a result, the technique might be included in the most commonly used tools for the metagenomic research. According to the outcomes, it could be utilized to effectively classify either SG or AMP information.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-231897