References
- Beaulieu A, Leonelli S (2021). Data and Society: A Critical Introduction. SAGE Publications Sage CA: Los Angeles, CA.
- Berg G, Rybakova D, Fischer D et al (2020) Microbiome definition re-visited: Old concepts and new challenges. Microbiome 8:103.
https://doi.org/10.1186/s40168-020-00875-0 - Bhattacharya S, Andorf S, Gomes L et al (2014) ImmPort: Disseminating data to the public for the future of immunology. Immunol Res 58:234–239.
https://doi.org/10.1007/s12026-014-8516-1 - Bhattacharya S, Dunn P, Thomas CG et al (2018) ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci Data 5:180015.
https://doi.org/10.1038/sdata.2018.15 - Bluestone JA, Tang Q (2015) Immunotherapy: Making the case for precision medicine. Sci Transl Med 7:280ed3.
https://doi.org/10.1126/scitranslmed.aaa9846 - Cao Z, Sugimura N, Burgermeister E et al (2022) The gut virome: A new microbiome component in health and disease. EBioMedicine 81:104113.
https://doi.org/10.1016/j.ebiom.2022.104113 - Chu X, Zhang B, Koeken VACM et al (2021) Multi-omics approaches in immunological research. Front Immunol 12:668045.
https://doi.org/10.3389/fimmu.2021.668045 - Cullen T, Garcia JE (2021) Data mining, data analytics, and bioinformatics. In: Okpaku SO (ed) Innovations in Global Mental Health. Springer, Cham, pp 455–488.
https://doi.org/10.1007/978-3-030-57296-9_141 - Deng N, Wu C, Yaseen A et al (2022) ImmuneData: An integrated data discovery system for immunology data repositories. Database 2022:baac003.
https://doi.org/10.1093/database/baac003 - Editorial (2015) Big data meets mechanism. Nat Med 21:673.
https://doi.org/10.1038/nm.3903 - Fu J, Li K, Zhang W et al (2020) Large-scale public data reuse to model immunotherapy response and resistance. Genome Med 12:21.
https://doi.org/10.1186/s13073-020-0721-z - Garattini C, Raffle J, Aisyah DN et al (2019) Big data analytics, infectious diseases and associated ethical impacts. Philos Technol 32:69–85.
https://doi.org/10.1007/s13347-017-0278-y - Heymann DL, Rodier GR (2004) SARS: A global response to an international threat. Brown J World Aff 10:185–197.
- Hong M, Clubb JD, Chen YY (2020) Engineering CAR-T cells for next-generation cancer therapy. Cancer Cell 38:473–488.
https://doi.org/10.1016/j.ccell.2020.07.005 - Hooper LV, Littman DR, Macpherson AJ (2012) Interactions between the microbiota and the immune system. Science 336: 1268–1273.
https://doi.org/10.1126/science.1223490 - Human Immunome Project (2023) A new model for global health: Harnessing the power of the immune system to improve health for all. Available at:
https://www.humanimmunomeproject.org/ and was accessed on 27 October 2023. - Jabbari P, Rezaei N (2019) Artificial intelligence and immunotherapy. Expert Rev Clin Immunol 15:689–691.
https://doi.org/10.1080/1744666X.2019.1623670 - Jasanoff S (2016) The Ethics of Invention: Technology and the Human Future. WW Norton & Company Inc, New York, NY.
- Jiang P, Sinha S, Aldape K et al (2022) Big data in basic and translational cancer research. Nat Rev Cancer 22:625–639.
https://doi.org/10.1038/s41568-022-00502-0 - Johnson KB, Wei WQ, Weeraratne D et al (2021) Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 14:86–93.
https://doi.org/10.1111/cts.12884 - Kaczorowski KJ, Shekhar K, Nkulikiyimfura D et al (2017) Continuous immunotypes describe human immune variation and predict diverse responses. Proc Natl Acad Sci USA 114:E6097–E6106.
https://doi.org/10.1073/pnas.1705065114 - Li J, Wu J, Zhao Z et al (2021a) Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: A narrative review. J Thorac Dis 13:7021–7033.
https://doi.org/10.21037/jtd-21-864 - Li Y, Ma L, Wu D et al (2021b) Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform 22:bbab024.
https://doi.org/10.1093/bib/bbab024 - Liu X, Hasan MR, Ahmed KA et al (2023) Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis. BMC Bioinformatics 24:7.
https://doi.org/10.1186/s12859-022-05127-6 - Lockey S, Gillespie N, Holm D et al (2021) A review of trust in artificial intelligence: Challenges, vulnerabilities and future directions. In: Proceedings of the 54th Hawaii International Conference on System Sciences, 5463–5472.
https://hdl.handle.net/10125/71284 - Maecker HT, McCoy JP, Nussenblatt R (2012) Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol 12:191–200.
https://doi.org/10.1038/nri3158 - Naqa IE, Kosorok MR, Jin J et al (2018) Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inform 2:CCI.18.00002.
https://doi.org/10.1200/CCI.18.00002 - Nikoopour E, Singh B (2014) Reciprocity in microbiome and immune system interactions and its implications in disease and health. Inflamm Allergy Drug Targets 13:94–104.
https://doi.org/10.2174/1871528113666140330201056 - Park JS, Gazzaniga FS, Kasper DL et al (2023) Microbiota-dependent regulation of costimulatory and coinhibitory pathways via innate immune sensors and implications for immunotherapy. Exp Mol Med 55:1913–1921.
https://doi.org/10.1038/s12276-023-01075-0 - Park SS, Grayson MH (2008) Clinical research: Protection of the “vulnerable”? J Allergy Clin Immunol 121:1103–1107.
- Parker DM, Pine SG, Ernst ZW (2019) Privacy and informed consent for research in the age of big data. Penn State Law Rev 123:703–733.
- Parvizpour S, Pourseif MM, Razmara J et al (2020) Epitope-based vaccine design: A comprehensive overview of bioinformatics approaches. Drug Discov Today 25:1034–1042.
https://doi.org/10.1016/j.drudis.2020.03.006 - Rider NL, Srinivasan R, Khoury P (2020) Artificial intelligence and the hunt for immunological disorders. Curr Opin Allergy Clin Immunol 20:565–573.
https://doi.org/10.1097/ACI.000000000000069 - Sadelain M, Rivière I, Riddell S (2017) Therapeutic T cell engineering. Nature 545:423–431.
https://doi.org/10.1038/nature22395 - Schultze JL (2015) Teaching ‘big data’ analysis to young immunologists. Nat Immunol 16:902–905.
https://doi.org/10.1038/ni.3250 - Sharma P, Goswami S, Raychaudhuri D et al (2023) Immune checkpoint therapy-current perspectives and future directions. Cell 186:1652–1669.
https://doi.org/10.1016/j.cell.2023.03.006 - Singh B, Qin N, Reid G (2015) Microbiome regulation of autoimmune, gut and liver associated diseases. Inflamm Allergy Drug Targets 14:84–93.
https://doi.org/10.2174/1871528114666160128150747 - Singh B, Summers K, Barker G et al (2019) Emergence of human immunoprofiling in health and disease. Curr Trends Immunol 20:11–19.
- Wadden JJ (2021) Defining the undefinable: The black box problem in healthcare artificial intelligence. J Med Ethics 48:764–768.
https://doi.org/10.1136/medethics-2021-107529 - Wang X, Fan D, Yang Y et al (2023) Integrative multi-omics approaches to explore immune cell functions: Challenges and opportunities. iScience 26:106359.
https://doi.org/10.1016/j.isci.2023.106359 - Xie J, Luo X, Deng X et al (2023) Advances in artificial intelligence to predict cancer immunotherapy efficacy. Front Immunol 13:1076883.
https://doi.org/10.3389/fimmu.2022.1076883 - Yu J, Peng J, Chi H (2019) Systems immunology: Integrating multi-omics data to infer regulatory networks and hidden drivers of immunity. Curr Opin Syst Biol 15:19–29.
https://doi.org/10.1016/j.coisb.2019.03.003 - Yurina V, Adianingsih OR (2022) Predicting epitopes for vaccine development using bioinformatics tools. Ther Adv Vaccines Immunother 10:25151355221100218.
https://doi.org/10.1177/25151355221100218 - Zhang GL, Sun J, Chitkushev L et al (2014) Big data analytics in immunology: A knowledge-based approach. Biomed Res Int 2014:437987.
https://doi.org/10.1155/2014/437987 - Zheng D, Liwinski T, Elinav E (2020) Interaction between microbiota and immunity in health and disease. Cell Res 30:492–506.
https://doi.org/10.1038/s41422-020-0332-7