Discovering Disease Biomarkers in Metabolomics via Big Data Analytics - Manik, Mia Md Tofayel Gonee; Nilima, Sadia Islam; Mahmud, Md Abdullah Al; Sharmin, Sadia; Hasan, Rakibul (American Journal of Statistics and Actuarial Sciences, 2022)
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.