Interventions of Immunobiology, AI, Bioinformatics, and Predictive Modeling in Oncology
DOI:
https://doi.org/10.47611/jsrhs.v13i3.6925Keywords:
Immunogenomics, Natural Language Processing, Cancer, Machine Learning, Immunophenotyping, T-cellsAbstract
This publication will centralize upon the various subsets of research behind immunogenetics, immunobiology, cancer research, immunophenotyping as well as predictive models to garner further research behind cancer research and oncology. Intrinsically, this publication will provide a comprehensive evaluation of both perspectives derived by oncologists, microbiologists as well as analysts to better comprehend how researchers can better improve clinical practices, trials, and outcomes in the years to come and biomedical advancements to be evolved. While this publication will mostly focus on the early diagnostic tools, biomedical tools, and forms of bioinformatics, it will also similarly touch upon subtopics related to interventions that are based upon Artificial Intelligence and Machine Learning tools to not only lead to better and more efficient onset of the disease but also potential outcomes and results that could be formulated. Similarly, this publication will also focus on potential ethical implications and limitations that this research behind cancer research, diagnostics, and interventions may have on current patients, future research, and R&D as well as on the medical community.
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Butner, Joseph D., et al. "Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy." Science advances 6.18 (2020): eaay6298.
Craig, Fiona E., and Kenneth A. Foon. "Flow cytometric immunophenotyping for hematologic neoplasms." Blood, The Journal of the American Society of Hematology 111.8 (2008): 3941-3967.
Conrads, Thomas P., et al. "Cancer diagnosis using proCraig, Fiona E., and Kenneth A. Foon. "Flow cytometric immunophenotyping for hematologic neoplasms." Blood, The Journal of the American Society of Hematology 111.8 (2008): 3941-3967.eomic patterns." Expert review of molecular diagnostics 3.4 (2023): 411-420.
Lal, Neeraj, et al. "An immunogenomic stratification of colorectal cancer: implications for development of targeted immunotherapy." Oncoimmunology 4.3 (2015): e976052.
Liao, Jinzhuang, et al. "Artificial intelligence assists precision medicine in cancer treatment." Frontiers in oncology 12 (2023): 998222.
Nelakurthi, Vidya Maheswari, Priyanka Paul, and Amit Reche. "Bioinformatics in Early Cancer Detection." Cureus 15.10 (2023).
Park, Wooram, Young-Jae Heo, and Dong Keun Han. "New opportunities for nanoparticles in cancer immunotherapy." Biomaterials research 22.1 (2018): 24.
Sethi, Prerna, and Kimberly Theodos. "Translational bioinformatics and healthcare informatics: computational and ethical challenges." Perspectives in Health Information Management/AHIMA, American Health Information Management Association 6.Fall (2009)
Xue, Lulu, et al. "Responsive biomaterials: optimizing control of cancer immunotherapy." Nature Reviews Materials 9.2 (2024): 100-118.
Yim, Wen-wai, et al. "Natural language processing in oncology: a review." JAMA oncology 2.6 (2016): 797-804.
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