Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Abstract

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Improved awareness of national genomic initiatives is needed to facilitate genomic education and training in Wales for pharmacists

“…Although the future potential of genomics as a means to transform healthcare and the healthcare system more broadly is well documented (Hassan et al, 2022).…”

Personalized Medicine: A Solution for Today and Tomorrow

“…Big data, which is mainly the visualization of data (43,44), has changed the biomedical research pathway since it was first introduced in 1997. Collecting data is much faster than analyzing the data; therefore, molecular integration is essential when considering electronic health records (43,45).…”

“…Collecting data is much faster than analyzing the data; therefore, molecular integration is essential when considering electronic health records (43,45). Data collection and analysis are crucial for identifying electronic health records and for drug and biomarker improvements of neurodegenerative diseases, cancers, cardiovascular diseases, and other critical or noncritical diseases (44). The first human genome sequence got the door to customizing the medicine in 2008 (46,47).…”

Adaptation of the binding domain ofLactobacillus acidophilusS-layer protein as a molecular tag for affinity chromatography development

“…The amalgamation of genomics, transcriptomics, proteomics, metabolomics, synthetic biology, and artificial intelligence has become a powerful approach to understand the complexity of any biological process, uncovering uncharacterized molecular mechanisms 1 2 3 .…”