Integrating machine learning and data analysis for predictive microbial community profiling

Document Type : Research Paper

Authors

1 Atyrau University named after Kh. Dosmukhamedov Atyrau, Kazakhstan & Atyrau, Studenchesky Ave., 1 0 Atyrau, Studenchesky Ave, 060000 Atyrau, the Republic of Kazakhstan

2 Khalel Dosmukhamedov Atyrau University, 060011 Atyrau, student Ave., 212, Atyrau city, Kazakhstan

3 Institute of Natural Sciences and Geography of the Kazakh National Pedagogical University named after Abai, Dostyk Ave., 13, Almaty, Kazakhstan

4 Institute of Natural Sciences and Geography, Abai Kazakh National Pedagogical University, Dostyk Av., Almaty, Kazakhstan

5 Department of Biology, Institute of Natural Sciences and Geography, Abai Kazakh National Pedagogical University, 13, Dostyk Av., 050010, Almaty, Kazakhstan

6 Institute of Natural Sciences and Geography, Abai Kazakh National pedagogical university, 13, Dostyk Av., 050010, Almaty, Kazakhstan,

7 High School of Natural Sciences of Astana International University, 8 Kabanbay Batyra Av., 000010, Astana, Kazakhstan

10.22124/cjes.2023.7413

Abstract

Microbiome research has gained prominence for its crucial role in various domains, from human health to environmental ecosystems. Understanding and predicting microbial community composition is essential for unlocking the potential of microbiomes. In this paper, we present a novel approach that leverages the synergy between machine learning and data analysis techniques to comprehensively profile and predict microbial communities. Our study addresses the current challenges in microbiome analysis by proposing a unified framework that integrates multiple data types, including 16S rRNA gene sequencing, metagenomic, and environmental data. We employ advanced machine learning algorithms, such as deep learning models and ensemble techniques, to extract meaningful patterns and relationships from these complex datasets. This integrated approach not only captures the taxonomic composition of microbial communities but also reveals functional potentials and ecological interactions among microbial taxa. One of the key novelties of our work lies in the development of a predictive model for microbial community assembly. By incorporating ecological principles and community dynamics, our model can forecast how microbial communities respond to environmental changes or perturbations, providing valuable insights for ecosystem management and restoration efforts. Furthermore, we demonstrate the practical applicability of our approach in diverse scenarios, including clinical microbiology, environmental monitoring, and biotechnological processes. We showcase its accuracy in predicting shifts in microbial community structure under varying conditions, offering a powerful tool for preemptive interventions in disease prevention and bioprocess optimization. We introduce an innovative methodology that bridges the gap between microbiology and machine learning, facilitating a deeper understanding of microbial ecosystems and their functional roles. By unifying data analysis and predictive modeling, our approach has the potential to revolutionize the way we study and harness the power of microbiomes, with far-reaching implications in healthcare, agriculture, and environmental conservation.

Keywords

References

Benn, AML, Heng, NCK, Broadbent, JM & Thomson, WM 2018, Studying the human oral microbiome: challenges and the evolution of solutions. Australian Dental Journal, 63: 14-24.
Berg, G, Rybakova, D, Fischer, D, Cernava, T, Vergès, MCC, Charles, T & Schloter, M 2020, Microbiome definition re-visited: Old concepts and new challenges. Microbiome, 8: 1-22.
Bharti, R & Grimm, DG 2021, Current challenges and best-practice protocols for microbiome analysis. Briefings in Bioinformatics, 22: 178-193.
Bissett, A, Brown, MV, Siciliano, SD, & Thrall, PH 2013, Microbial community responses to anthropogenically induced environmental change: towards a systems approach. Ecology Letters, 16: 128-139.
Cai, L, Li, H, Deng, J, Zhou, R & Zeng, Q 2023 Biological interactions with Prochlorococcus: implications for the marine carbon cycle. Trends in Microbiology. https://doi.org/10.1016/j.tim.2023.08.011
Cammarota, G, Ianiro, G, Ahern, A, Carbone, C, Temko, A, Claesson, MJ  & Tortora, G 2020, Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nature Reviews Gastroenterology & Hepatology, 17: 635-648.
Chattopadhyay, I, Lu, W, Manikam, R, Malarvili, MB, Ambati, RR & Gundamaraju, R 2023, Can metagenomics unravel the impact of oral bacteriome in human diseases? Biotechnology and Genetic Engineering Reviews, 39: 85-117.
Comin, M, Di Camillo, B, Pizzi, C & Vandin, F 2021 Comparison of microbiome samples: Methods and computational challenges. Briefings in Bioinformatics, 22: 88-95.
Corner, RD, Cribb, TH & Cutmore, SC 2023 Rich but morphologically problematic: an integrative approach to taxonomic resolution of the genus Neospirorchis Trematoda: Schistosomatoidea. International Journal for Parasitology, 53: 363-380.
Davenport, F, Gallacher, J, Kourtzi, Z, Koychev, I, Matthews, PM, Oxtoby, NP & Zetterberg, H 2023, Neurodegenerative disease of the brain: a survey of interdisciplinary approaches. Journal of the Royal Society Interface, 20: 20220406.
De Meaux, J & Mitchell-Olds, T 2003, Evolution of plant resistance at the molecular level: ecological context of species interactions. Heredity, 91: 345-352.
de Vasconcelos Gomes, LA, de Faria, AM, Braz, AC, de Mello, AM, Borini, FM & Ometto, AR 2023, Circular ecosystem management: Orchestrating ecosystem value proposition and configuration. International Journal of Production Economics, 256: 108725.
Ebrahimi, SS, Pourbabaei, H & Pothier, D 2018, The effect of grazing and anthropogenic disturbances on floristic and physiognomic characteristics in oriental beech communities, Masal Forest, Iran. Caspian Journal of Environmental Sciences, 16: 319-332.
Eicher, T, Kinnebrew, G, Patt, A, Spencer, K, Ying, K, Ma, Q  & Mathé, E A 2020, Metabolomics and multi-omics integration: a survey of computational methods and resources. Metabolites, 10: 202.
Graw, S, Chappell, K, Washam, CL, Gies, A, Bird, J, Robeson, MS & Byrum, SD 2021, Multi-omics data integration considerations and study design for biological systems and disease. Molecular Omics, 17: 170-185.
Harris, J 2009 Soil microbial communities and restoration ecology: facilitators or followers? Science, 325: 573-574.
Hewavitharana, SS, Klarer, E, Reed, A J, Leisso, R, Poirier, B, Honaas, L & Mazzola, M 2019, Temporal dynamics of the soil metabolome and microbiome during simulated anaerobic soil disinfestation. Frontiers in Microbiology, 10: 2365.
Hu, Y, Łukasik, P, Moreau, CS & Russell, JA 2014, Correlates of gut community composition across an ant species (C. ephalotes varians) elucidate causes and consequences of symbiotic variability. Molecular Ecology, 23: 1284-1300.
Idrisovich Ismagilov, I, Ayratovich Murtazin, A, Vladimirovna Kataseva, D, Sergeevich Katasev, A & Olegovna Barinova, A 2020, Formation of a knowledge base to analyze the issue of transport and the environment. Caspian Journal of Environmental Sciences, 18: 615-621.
Janda, JM & Abbott, SL 2007 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. Journal of Clinical Microbiology, 45: 2761-2764.
Javidan, P, Baghdadi, M, Torabian, A & Goharrizi, BA 2022, A tailored metal–organic framework applicable at natural pH for the removal of 17α-ethinylestradiol from surface water. Cancer, 11: 13.
Kaul, A, Davidov, O & Peddada, SD 2017 Structural zeros in high-dimensional data with applications to microbiome studies. Biostatistics, 18: 422-433.
Mohammed Al-Shemmary, AJ & Salih Al-Taee, MM 2021, Response of sorghum to effect of two azo dye bacteria. Caspian Journal of Environmental Sciences, 19: 251-260.
Molloy, MP, Brzezinski, EE, Hang, J, McDowell, MT & VanBogelen, R A 2003 Overcoming technical variation and biological variation in quantitative proteomics. Proteomics, 3, 1912-1919.
Narayanan, M, Ali, SS & El-Sheekh, M 2023 A comprehensive review on the potential of microbial enzymes in multipollutant bioremediation: Mechanisms, challenges, and future prospects. Journal of Environmental Management, 334: 117532.
Nejatian, N, Yavary Nia, M, Yousefyani, H, Shacheri, F & Yavari Nia, M 2023, The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershed. Water Science and Technology, 87: 1791-1802.
Odom, AR, Faits, T, Castro-Nallar, E, Crandall, KA & Johnson, WE 2023, Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data. Scientific Reports, 13: 13957.
Polia, F, Pastor-Belda, M, Martínez-Blázquez, A, Horcajada, MN, Tomás-Barberán, FA & García-Villalba, R 2022, Technological and biotechnological processes to enhance the bioavailability of dietary poly phenols in humans. Journal of Agricultural and Food Chemistry, 70: 2092-2107.
Poussin, C, Sierro, N, Boué, S, Battey, J, Scotti, E, Belcastro, V & Hoeng, J 2018 Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discovery Today, 23, 1644-1657.
Qu, F, Cheng, H, Han, Z, Wei, Z & Song, C 2023 Identification of driving factors of lignocellulose degrading enzyme genes in different microbial communities during rice straw composting. Bioresource Technology, 381: 129109.
Rohlfing, C, Wiedmeyer, HM, Little, R, Grotz, VL, Tennill, A, England, J & Goldstein, D 2002 Biological variation of glycohemoglobin. Clinical Chemistry, 48: 1116-1118.
Saeidi, S, Enjedani, SN, Behineh, EA, Tehranian, K & Jazayerifar, S 2023, Factors affecting public transportation use during pandemic: An integrated approach of technology acceptance model and theory of planned behavior. Tehnički glasnik, 18:1-12, DOI:10.31803/tg-20230601145322
Sze, MA & Schloss, PD 2016, Looking for a signal in the noise: revisiting obesity and the microbiome. MBio, 7: 1110-1128, DOI: https://doi.org/10.1128/mbio.01018-16
Tehranian, K 2023a, Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments? arXiv preprint arXiv:2308.16200.
Tehranian, K 2023b, Monetary Policy & Stock Market. arXiv preprint arXiv:2305.13930.
Thatoi, H, Behera, B C, Mishra, R R, & Dutta, S K 2013, Biodiversity and biotechnological potential of microorganisms from mangrove ecosystems: a review. Annals of Microbiology, 63: 1-19.
Wang, X, Feng, J, Ao, G, Qin, W, Han, M, Shen, Y & Zhu, B 2023a, Globally nitrogen addition alters soil microbial community structure, but has minor effects on soil microbial diversity and richness. Soil Biology and Biochemistry, 179: 108982.
Wang, H, Liu, X, Wang, Y, Zhang, S, Zhang, G, Han, Y & Liu, L 2023b, Spatial and temporal dynamics of microbial community composition and factors influencing the surface water and sediments of urban rivers. Journal of Environmental Sciences, 124: 187-197.
Worby, CJ, Sridhar, S, Turbett, SE, Becker, MV, Kogut, L, Sanchez, V & LaRocque, RC 2023, Gut microbiome perturbation, antibiotic resistance, and Escherichia coli strain dynamics associated with international travel: a metagenomic analysis. The Lancet Microbe, 4: e790-e799.
Xu, Z & Knight, R 2015, Dietary effects on human gut microbiome diversity. British Journal of Nutrition, 113: S1-S5.
Caspian Journal of Environmental Sciences
Volume 21, Issue 5
Special Issue: Enviroment & Economy
December 2023
Pages 1209-1227

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