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Reliable Biomedical Applications Using AI Models
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Shambhavi Mishra, Tanveer Ahmed, Vipul Mishra
Studies based on genomics sequencing and gene expression directed towards protein structure prediction fall under the biomedical sector. Several studies show omics work on genomics, but other applications such as biomedicine and bioinformatics can also be found. Omics covers genetic data such as protein, metabol, gen, transcript, and epigen. It also concerns protein–protein interactions (PPIs). The authors of [39] studied different statistical learning framework methods that are integrated with different multidisciplinary areas including biology, machine learning, and AI. In the literature, PCA, clustering methods, regularization-based methods, regression methods, and knowledge enhancement learning have all been investigated and analyzed. The limitations and strengths of multiple standard ML methods are also discussed. According to [40]’s research, image data alone is insufficient for analyzing complicated disorders and obtaining an appropriate diagnosis. In parallel with large, high-quality data sets, domain knowledge and the requirement for multiple networks is also important. While high-dimensional data will always yield better results, all three components are crucial for providing robust ML model training and validation. The authors of one of the studies[41] looked at various AI-based approaches to analyzing different types of cancer.
Big data and public health
Published in Sridhar Venkatapuram, Alex Broadbent, The Routledge Handbook of Philosophy of Public Health, 2023
Instead of studying specific genes, proteins, neural connections, and so forth, scientists in the omics disciplines study all instances of those entities in an organism: human genomics investigates our genetic material in its entirety, proteomics investigates all proteins, and connectomics investigates all neural connections and circuits. The advent of genomics was occasioned by the plummeting cost of sequencing a person’s whole genome over the past two decades, from around US$100,000,000 to around US$1,000 (Prosperi et al. 2018). Research enabled by advances in sequencing technology resulted in the American College of Medical Genetics and Genomics’ publication of a list of 56 actionable genes in 2013, updated to 59 in 2016. Specific variants of these genes are known to produce disorders for which early intervention is possible. Therefore, if one of these variants is detected as a secondary finding on any genetic test, there is compelling reason to inform the individual being tested so that appropriate action may be taken (Kalia et al. 2017). Advances in genetic sequencing have also enabled direct-to-consumer genetic tests, which present privacy concerns when companies can collect and store consumers’ genetic information.
Medicinal Plants: Future Thrust Areas and Research Directions
Published in Amit Baran Sharangi, K. V. Peter, Medicinal Plants, 2023
The post-genomic period witnessed the emergence of “omic” studies in biological research. System science and omics-biotechnology-driven strategies can be used potentially to unravel the yet to be untapped potential for novel molecular target discovery. An array of technology platforms, viz., System biology, Bioinformatics, Genomics, Proteomics, Transcriptomics, Metabolomics, Automated separation techniques, Computer-aided drug design, Transgenic, and RNAi technology, Biochip, and Automated separation techniques are there in course of herbal drug development through system biology. The understanding of the mechanism of action of herbal bioactive principles has introduced vistas of scientific methods for the modernization and standardization of several herbal medicines including those of Chinese (Buriani et al., 2012). Epigenomics, metagenomics, toxicogenomics, pharma-cogenomics, herbogenomics, metallomics, etc., are a few “omic” approaches at the genetic level. Newer approaches and insights into herbal medicine through Research and Development (R&D) led to development of abundant traditional remedies and ground-breaking drug discovery systems (Pang et al., 2011), which will make an immense impact on the typical biomedical science (Trusheim et al., 2007; Parekh et al., 2009).
The Kendall interaction filter for variable interaction screening in high dimensional classification problems
Published in Journal of Applied Statistics, 2023
Youssef Anzarmou, Abdallah Mkhadri, Karim Oualkacha
As stated earlier, most existing SIS-type feature screening methods process predictors to capture marginal main effects, ignoring features' interplay impact on the outcome. Accounting for important interaction effects can substantially contribute to explaining the outcome total variation and might help improve the prediction of many statistical learning models. In the genetics field, for instance, the study of gene-gene (i.e. epistasis) and gene-environment interactions has been a focus of research for several years [5,33]; such genetic interactions play a crucial role in the etiology, prognosis and response to treatment of many complex human diseases beyond the main effects [29]. Yet, with the emergence of Multi-omics data collection (genomics, epigenomics, trancriptomics, metabolomics), the interplay between DNA methylation (epigenomics marks) and near-by SNPs (genomics markers) in influencing the patterns of gene expression (transcriptomics profiles) is a focus of many recent pharmacogenomics applications to contribute to ‘precision medicine’ and treatment plans tailored to the genetic makeup of patients.
An overview of technologies for MS-based proteomics-centric multi-omics
Published in Expert Review of Proteomics, 2022
Andrew T. Rajczewski, Pratik D. Jagtap, Timothy J. Griffin
Bottom-up proteomics holds a valuable place within the hierarchy of ‘omics technologies, directly detecting the functional molecules that collectively drive biochemical mechanisms within a cell, tissue, or organism. While informative, proteome data is only one piece of the network of interconnected biomolecules responsible for cellular function and phenotypes. Integration with DNA or RNA sequencing information that may give rise to translated proteins, or metabolite information, which indicates their biochemical activity state, provides a more complete picture. Recent advances in bottom-up MS-based proteomics methodologies and instrumentation now make deeper characterization of the proteome a reality, improving the value of integration with other ‘omic data (e.g. DNA/RNA sequencing results). At the same time, bioinformatics tools have emerged to facilitate the analysis of large ‘omics data sets, including options for integration of MS-based proteomics data with other ‘omic levels of information. The linking of DNA and/or RNA NGS data with deep MS-based proteomics data has given rise to the area of proteogenomics, which offers promise in detecting previously unseen protein sequences belonging to proteoforms that may be key to biological processes and disease. As advances continue to make MS-based proteomics more cost-effective, sensitive and high-throughput, multi-omic analyses centered around these data have the potential to become a pillar of twenty-first century systems biology-based research – impacting diverse fields from translational clinical applications to the study of complex environmental phenomena.
Is Dupras and Bunnik’s Framework for Assessing Privacy Risks in Multi-Omic Research and Databases Still Too Exceptionalist?
Published in The American Journal of Bioethics, 2021
This being said, research on epigenomics including large epigenomic datasets and epigenome editing alike is but one kind of omics going currently rather unnoticed by normative analyses. Therefore, research that highlights the ethical importance of epigenomic data (Dupras and Bunnik 2021) and of epigenome editing (Alex and Winkler n.d; WHO Expert Advisory Committe on Developing Global Standards for Governance and Oversight of Human Genome Editing 2021; Zeps et al. 2021) is always at risk of reductionism and of replacing genomic exceptionalism, that is, “the view that genomic information deserves special attention in ethics guidelines, laws and politics” (Dupras and Bunnik 2021, 49) and the view of “reserving disproportionate ethical sensibility and scrutiny to these data types at the expense of others” (Dupras and Bunnik 2021, 58), with genomic–epigenomic exceptionalism, a view that places special attention to genomic and epigenomic data alike.