Discovering Disease Biomarkers in Metabolomics via Big Data Analytics
DOI:
https://doi.org/10.47672/ajsas.2452Keywords:
Big Data Analytics, Biomarker, Metabolomics, NMR, ChromatographyAbstract
Purpose: This paper aims to demonstrate the utility of BDA in metabolomics and illness biomarker identification. Additionally, it will describe the metabolomics methodology and procedures, big data for metabolomics, and biomarkers utilizing these strategies. Lastly, it will highlight metabolomics possible future course and advancement as well as its incorporation into the healthcare industry.
Materials and Methods: Metabolomics data is handled via big data, which also helps find illness biomarkers. The research is conducted with simple collective analysis.
Findings: The results have found that metabolomics is a formidable tool, and its implementation has significantly improved our capacity to evaluate disease assumptions.
Implications to Theory, Practice and Policy: In the future, metabolomics and big data can be combined to help resolve challenging medical problems. By combining metabolomics with advanced blockchain, artificial intelligence, and statistical and computational analysis, metabolomics can improve the identification and finding of biomarkers. Metabolomics is a formidable tool for developing and formulating precision with large data analysis, and its integration with other "omics fields" and the addition of new technology and initiatives will bring ideas of personalized medicine to life. As metabolomics continues to evolve, further discoveries can be made in the future, benefiting the understanding of disorders and the treatment of patients.
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Abdelnabi, S., Hasan, R., & Fritz, M. (2022). Open-domain, content-based, multi-modal fact-checking of out-of-context images via online resources. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,
Akid, A. S. M., Shah, S. M. A., Sobuz, M. D. H. R., Tam, V. W. Y., & Anik, S. H. (2021). Combined influence of waste steel fibre and fly ash on rheological and mechanical performance of fibre-reinforced concrete. Australian Journal of Civil Engineering, 19(2), 208-224. https://doi.org/10.1080/14488353.2020.1857927
Akid, A. S. M., Wasiew, Q. A., Sobuz, M. H. R., Rahman, T., & Tam, V. W. (2021). Flexural behavior of corroded reinforced concrete beam strengthened with jute fiber reinforced polymer. Advances in Structural Engineering, 24(7), 1269-1282. https://doi.org/10.1177/1369433220974783
Alonso, A., Marsal, S., & Julià, A. (2015). Analytical methods in untargeted metabolomics: state of the art in 2015. Frontiers in bioengineering and biotechnology, 3, 23. https://doi.org/https://doi.org/10.3389/fbioe.2015.00023
Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of big Data, 9(1), 3. https://doi.org/https://doi.org/10.1186/s40537-021-00553-4
Bekri, S. (2016). The role of metabolomics in precision medicine. Expert Review of Precision Medicine and Drug Development, 1(6), 517-532. https://doi.org/https://doi.org/10.1080/23808993.2016.1273067
Cremin, C. J., Dash, S., & Huang, X. (2022). Big data: historic advances and emerging trends in biomedical research. Current Research in Biotechnology, 4, 138-151. https://doi.org/https://doi.org/10.1016/j.crbiot.2022.02.004
Das, S., Habibur Rahman Sobuz, M., Tam, V. W. Y., Akid, A. S. M., Sutan, N. M., & Rahman, F. M. M. (2020). Effects of incorporating hybrid fibres on rheological and mechanical properties of fibre reinforced concrete. Construction and Building Materials, 262, 120561. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.120561
Datta, S. D., Islam, M., Rahman Sobuz, M. H., Ahmed, S., & Kar, M. (2024). Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review. Heliyon, 10(5), e26888. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e26888
Guasch-Ferré, M., Bhupathiraju, S. N., & Hu, F. B. (2018). Use of metabolomics in improving assessment of dietary intake. Clinical chemistry, 64(1), 82-98. https://doi.org/https://doi.org/10.1373/clinchem.2017.272344
Guo, J., Yu, H., Xing, S., & Huan, T. (2022). Addressing big data challenges in mass spectrometry-based metabolomics. Chemical Communications, 58(72), 9979-9990. https://doi.org/https://doi.org/10.1039/D2CC03598G
Hasan, N. M. S., Sobuz, M. H. R., Khan, M. M. H., Mim, N. J., Meraz, M. M., Datta, S. D., Rana, M. J., Saha, A., Akid, A. S. M., Mehedi, M. T., Houda, M., & Sutan, N. M. (2022). Integration of Rice Husk Ash as Supplementary Cementitious Material in the Production of Sustainable High-Strength Concrete. Materials, 15(22), 8171. https://www.mdpi.com/1996-1944/15/22/8171
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: a review of anomaly detection techniques and recent advances. Expert systems With applications, 193, 116429.
Jakhrani, A., Samo, S., Sobuz, H. R., Uddin, M. A., Ahsan, M., & Hasan, N. M. S. (2012). Assessment of dissolved salts concentration of seawater in the vicinity of Karachi. International Journal of Structural and Civil Engineering, 1(2), 61-69.
Jin, Q., & Ma, R. C. W. (2021). Metabolomics in diabetes and diabetic complications: insights from epidemiological studies. Cells, 10(11), 2832. https://doi.org/https://doi.org/10.3390/cells10112832
Liebal, U. W., Phan, A. N., Sudhakar, M., Raman, K., & Blank, L. M. (2020). Machine learning applications for mass spectrometry-based metabolomics. Metabolites, 10(6), 243. https://doi.org/https://doi.org/10.3390/metabo10060243
Nagana Gowda, G., & Raftery, D. (2021). NMR-based metabolomics. Cancer Metabolomics: Methods and Applications, 19-37. https://doi.org/https://doi.org/10.1007/978-3-030-51652-9_2
Nicholls, J., Kuppa, A., & Le-Khac, N.-A. (2021). Financial cybercrime: A comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. Ieee Access, 9, 163965-163986.
Odenkirk, M. T., Reif, D. M., & Baker, E. S. (2021). Multiomic big data analysis challenges: increasing confidence in the interpretation of artificial intelligence assessments. Analytical chemistry, 93(22), 7763-7773. https://doi.org/https://doi.org/10.1021/acs.analchem.0c04850
Perez De Souza, L., Alseekh, S., Brotman, Y., & Fernie, A. R. (2020). Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Review of Proteomics, 17(4), 243-255. https://doi.org/https://doi.org/10.1080/14789450.2020.1766975
Rana, J., Hasan, R., Sobuz, H. R., & Tam, V. W. Y. (2022). Impact assessment of window to wall ratio on energy consumption of an office building of subtropical monsoon climatic country Bangladesh. International Journal of Construction Management, 22(13), 2528-2553. https://doi.org/10.1080/15623599.2020.1808561
Rana, M. J., Hasan, M. R., & Sobuz, M. H. R. (2022). An investigation on the impact of shading devices on energy consumption of commercial buildings in the contexts of subtropical climate. Smart and Sustainable Built Environment, 11(3), 661-691. https://doi.org/10.1108/SASBE-09-2020-0131
Rana, M. J., Hasan, M. R., Sobuz, M. H. R., & Sutan, N. M. (2021). Evaluation of passive design strategies to achieve NZEB in the corporate facilities: The context of Bangladeshi subtropical monsoon climate. International Journal of Building Pathology and Adaptation, 39(4), 619-654.
Schmidt, D. R., Patel, R., Kirsch, D. G., Lewis, C. A., Vander Heiden, M. G., & Locasale, J. W. (2021). Metabolomics in cancer research and emerging applications in clinical oncology. CA: a cancer journal for clinicians, 71(4), 333-358. https://doi.org/https://doi.org/10.3322/caac.21670
Sobuz, M. H. R., Datta, S. D., & Rahman, M. (2022). Evaluating the Properties of Demolished Aggregate Concrete with Non-destructive Assessment. In S. Arthur, M. Saitoh, & S. K. Pal (Eds.), Advances in Civil Engineering, Lecture Notes in Civil Engineering (pp. 223-233). Springer Singapore. https://doi.org/10.1007/978-981-16-5547-0_22
Steuer, A. E., Brockbals, L., & Kraemer, T. (2019). Metabolomic strategies in biomarker research–new approach for indirect identification of drug consumption and sample manipulation in clinical and forensic toxicology? Frontiers in Chemistry, 7, 319. https://doi.org/https://doi.org/10.3389/fchem.2019.00319
Taherdoost, H. (2022). Blockchain technology and artificial intelligence together: a critical review on applications. Applied Sciences, 12(24), 12948. https://doi.org/https://doi.org/10.3390/app122412948
Tolani, P., Gupta, S., Yadav, K., Aggarwal, S., & Yadav, A. K. (2021). Big data, integrative omics and network biology. Advances in protein chemistry and structural biology, 127, 127-160. https://doi.org/https://doi.org/10.1016/bs.apcsb.2021.03.006
Van Ravenzwaay, B., Cunha, G. C.-P., Leibold, E., Looser, R., Mellert, W., Prokoudine, A., Walk, T., & Wiemer, J. (2007). The use of metabolomics for the discovery of new biomarkers of effect. Toxicology letters, 172(1-2), 21-28. https://doi.org/https://doi.org/10.1016/j.toxlet.2007.05.021
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