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

Article type: Research Article

Authors: Raman, Ramakrishnan^{a; *} | Barve, Amit^b | Meenakshi, R.^c | Jayaseelan, G.M.^d | Ganeshan, P.^e | Taqui, Syed Noeman^f | Almoallim, Hesham S.^g | Alharbi, Sulaiman Ali^h | Raghavan, S.S.ⁱ

Affiliations: [a] Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed) University, Pune, India | [b] Department of Computer Science and Engineering, Parul Institute of Technology, Faculty of Engineering and Technology, Parul University, P.O. Limda, Gujarat, India | [c] Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Tamil Nadu, India | [d] Skillsmith India, Ayapakkam, Chennai, Tamil Nadu, India | [e] Department of Mechanical Engineering, Sri Eshwar Engineering College, Coimbatore, Tamilnadu, India | [f] Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India | [g] Department of Oral and Maxillofacial Surgery, College of Dentistry, King Saud University, Riyadh, Saudi Arabia | [h] Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia | [i] Department of Biology, University of Tennessee Health Science center, Memphis, USA

Correspondence: [*] Corresponding author. Ramakrishnan Raman, Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed) University, Pune, India. E-mail: [email protected].

Abstract: 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.

Keywords: Deep neural network, RNA virus, metagenomic, convolutional neural network (CNN), taxonomic classification, Deep Belief Network (DBN), K-mer Representation

DOI: 10.3233/JIFS-231897

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7603-7618, 2023