{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T21:44:12Z","timestamp":1783547052864,"version":"3.55.0"},"reference-count":137,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.<\/jats:p>","DOI":"10.3390\/s21237786","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4507-1844","authenticated-orcid":false,"given":"Sharnil","family":"Pandya","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aanchal","family":"Thakur","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4632-6156","authenticated-orcid":false,"given":"Santosh","family":"Saxena","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandita","family":"Jassal","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8280-1140","authenticated-orcid":false,"given":"Chirag","family":"Patel","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research, Changa 388421, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6462-059X","authenticated-orcid":false,"given":"Kirit","family":"Modi","sequence":"additional","affiliation":[{"name":"Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pooja","family":"Shah","sequence":"additional","affiliation":[{"name":"Information Technology Department, Gandhinagar Institute of Technology, Ahmedabad 382010, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5871-890X","authenticated-orcid":false,"given":"Rahul","family":"Joshi","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudhanshu","family":"Gonge","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalyani","family":"Kadam","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prachi","family":"Kadam","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","unstructured":"Rehman, M.U., Andargoli, A.E., and Pousti, H. (2019, January 9\u201311). Healthcare 4.0: Trends, Challenges and Benefits. Proceedings of the 30th Australasian Conference on Information Systems, Perth, Australia."},{"key":"ref_2","first-page":"348","article-title":"Key Trends in Healthcare for 2020 and Beyond","volume":"12","author":"Vogenberg","year":"2019","journal-title":"Am. Health Drug Benefits"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.hlpt.2021.100543","article-title":"The implications of the California Consumer Privacy Act (CCPA) on healthcare organizations: Lessons learned from early compliance experiences","volume":"10","author":"Mulgund","year":"2021","journal-title":"Health Policy Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.gaceta.2020.12.019","article-title":"Artificial intelligence in healthcare: Opportunities and risk for future","volume":"35","author":"Sunarti","year":"2021","journal-title":"Gac. Sanit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.clinthera.2015.12.001","article-title":"IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research","volume":"38","author":"Chen","year":"2016","journal-title":"Clin. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"305","DOI":"10.3389\/fmed.2018.00305","article-title":"Evaluating Clinical Genome Sequence Analysis by Watson for Genomics","volume":"5","author":"Itahashi","year":"2018","journal-title":"Front. Med."},{"key":"ref_7","unstructured":"Copeland, B.J. (2021). Artificial intelligence. Encyclopedia Britannica, Encyclopedia Britannica Inc."},{"key":"ref_8","first-page":"277","article-title":"The antibiotic resistance crisis: Part 1: Causes and threats","volume":"40","author":"Ventola","year":"2015","journal-title":"Pharm. Ther."},{"key":"ref_9","unstructured":"Szolovits, P. (1982). Representation of Expert Knowledge for Consultation: The CASNET and EXPERT Projects. Artificial Intelligence in Medicine, Westview Press. Chapter 2."},{"key":"ref_10","first-page":"59","article-title":"Building Watson: An Overview of the DeepQA Project","volume":"31","author":"Ferrucci","year":"2010","journal-title":"AI Mag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ni, L., Lu, C., Liu, N., and Liu, J. (2017). MANDY: Towards a Smart Primary Care Chatbot Application. Knowledge and Systems Sciences. KSS. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-981-10-6989-5_4"},{"key":"ref_12","unstructured":"Ricardo, B., Rieg, T., and Frick, J. (2020). Machine learning based diagnosis of diseases using the unfolded EEG spectra: Towards an intelligent software sensor. Information Systems and Neuroscience, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yue, W., Wang, Z., Chen, H., Payne, A., and Liu, X. (2018). Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis. Designs, 2.","DOI":"10.3390\/designs2020013"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16444","DOI":"10.1038\/s41598-018-34753-5","article-title":"Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy","volume":"8","author":"Huang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wang, S., Zhu, F., and Huang, J. (2017, January 20\u201323). Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Boston, MA, USA.","DOI":"10.1145\/3107411.3107424"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101238","DOI":"10.1016\/j.aei.2020.101238","article-title":"Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence","volume":"47","author":"Pandya","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.4172\/2329-6887.1000e173","article-title":"Artificial Intelligence in Drug Discovery and Development","volume":"6","author":"Agrawal","year":"2018","journal-title":"J. Pharmacovigil."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s40264-019-00831-4","article-title":"Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning","volume":"42","author":"Barmaz","year":"2019","journal-title":"Drug Saf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lee, D., and Yoon, S.N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18010271"},{"key":"ref_21","first-page":"25","article-title":"Chapter 2\u2014The rise of artificial intelligence in healthcare applications","volume":"20","author":"Bohr","year":"2020","journal-title":"Artif. Intell. Healthc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies in medical imaging","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102798","DOI":"10.1016\/j.scs.2021.102798","article-title":"Recognizing suspect and predicting the spread of contagion based on mobile phone location data (counteract): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence","volume":"69","author":"Ghayvat","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3326","DOI":"10.1002\/hed.26384","article-title":"Highly conformal reirradiation in patients with prior oropharyngeal radiation: Clinical efficacy and toxicity outcomes","volume":"42","author":"Bagley","year":"2020","journal-title":"Head Neck"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.canlet.2016.05.033","article-title":"Big Data and machine learning in radiation oncology: State of the art and future prospects","volume":"382","author":"Bibault","year":"2016","journal-title":"Cancer Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1634\/theoncologist.2018-0255","article-title":"Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China","volume":"24","author":"Zhou","year":"2019","journal-title":"Oncologist"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kyrarini, M., Lygerakis, F., Rajavenkatanarayanan, A., Sevastopoulos, C., Nambiappan, H.R., Chaitanya, K.K., Badu, A.R., Mathew, J., and Makedon, F. (2021). A Survey of Robots in Healthcare. Technologies, 9.","DOI":"10.3390\/technologies9010008"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mulfari, D., Celesti, A., Fazio, M., Villari, M., and Puliafito, A. (2016, January 27\u201330). Using google cloud vision in assistive technology scenarios. Proceedings of the IEEE Symposium on Computers and Communications, Messina, Italy.","DOI":"10.1109\/ISCC.2016.7543742"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ghayvat, H., Awais, M., Pandya, S., Ren, H., Akbarzadeh, S., Chandra Mukhopadhyay, S., Chen, C., Gope, P., Chouhan, A., and Chen, W. (2019). Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors, 19.","DOI":"10.3390\/s19040766"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1089\/dia.2015.0394","article-title":"Comparison of Two Continuous Glucose Monitoring Systems, Dexcom G4 Platinum and Medtronic Paradigm Veo Enlite System, at Rest and During Exercise","volume":"18","author":"Taleb","year":"2016","journal-title":"Diabetes Technol. Ther."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40745-020-00314-9","article-title":"Mehendale, N. Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning","volume":"8","author":"Khakharia","year":"2021","journal-title":"Ann. Data. Sci."},{"key":"ref_32","first-page":"227","article-title":"ProMED-mail: An early warning system for emerging diseases","volume":"15","author":"Madoff","year":"2004","journal-title":"Clin. Infect. Dis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.eng.2019.08.015","article-title":"Artificial Intelligence in Healthcare: Review and Prediction Case Studies","volume":"6","author":"Rong","year":"2020","journal-title":"Engineering"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., and Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01488-9"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The potential for artificial intelligence in healthcare","volume":"6","author":"Davenport","year":"2019","journal-title":"Future Healthc. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: A survey study from consumers\u2019 perspectives. BMC Med. Inf. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01191-1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101822","DOI":"10.1016\/j.artmed.2020.101822","article-title":"Automated machine learning: Review of the state-of-the-art and opportunities for healthcare","volume":"104","author":"Waring","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e15154","DOI":"10.2196\/15154","article-title":"Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians","volume":"22","author":"Asan","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e15154","DOI":"10.2196\/25759","article-title":"Role of Artificial Intelligence Application in Real-Life Clinical Practice: Systematic Review","volume":"23","author":"Yin","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bartoletti, I. (2019, January 26\u201329). AI in healthcare: Ethical and privacy challenges. Proceedings of the Conference on Artificial Intelligence in Medicine in Europe, Poznan, Poland.","DOI":"10.1007\/978-3-030-21642-9_2"},{"key":"ref_41","unstructured":"Gerke, S., Minssen, T., and Cohen, G. (2010). Chapter 12\u2014Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, Academic Press."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/BF00222709","article-title":"Why are some genetic diseases common?","volume":"91","author":"Flint","year":"1993","journal-title":"Hum. Genet."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S1471-4906(01)01867-1","article-title":"Immunology, climate change and vector-borne diseases","volume":"22","author":"Patz","year":"2001","journal-title":"Trends Immunol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1126\/science.1972595","article-title":"Autoimmune diseases: The failure of self-tolerance","volume":"248","author":"Sinha","year":"1990","journal-title":"Science"},{"key":"ref_46","unstructured":"Britannica (2021). Learn about the characteristics and harmful effects of fungi. Encyclopedia Britannica, Encyclopedia Britannica Inc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"387","DOI":"10.3109\/15563659709043371","article-title":"A Restrospective Study of Poisoning in Tehran","volume":"35","author":"Mohammad","year":"1997","journal-title":"J. Toxicol. Clin. Toxicol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1021\/tx9003787","article-title":"Toxicology of autoimmune diseases","volume":"23","author":"Pollard","year":"2010","journal-title":"Chem. Res. Toxicol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/S0749-0704(05)70063-4","article-title":"Radiation Injuries","volume":"15","author":"Reeves","year":"1999","journal-title":"Crit. Care Clin."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bookwalter, D.B., Roenfeldt, K.A., LeardMann, C.A., Kong, S.Y., Riddle, M.S., and Rull, R.P. (2020). Posttraumatic stress disorder and risk of selected autoimmune diseases among US military personnel. BMC Psychiatry, 20.","DOI":"10.1186\/s12888-020-2432-9"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.clim.2018.04.002","article-title":"Immune senescence, epigenetics and autoimmunity","volume":"196","author":"Ray","year":"2018","journal-title":"Clin. Immunol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/5.662875","article-title":"The visible human project","volume":"86","author":"Ackerman","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Meydani, S.N., Guo, W., Han, S.N., and Wu, D. (2020). Chapter 30\u2014Nutrition and autoimmune diseases. Present Knowledge in Nutrition, Academic Press. [11th ed.].","DOI":"10.1016\/B978-0-12-818460-8.00030-7"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1016\/j.jaci.2009.12.980","article-title":"Overview of the immune response","volume":"125","author":"Chaplin","year":"2010","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3389\/fpsyt.2019.00131","article-title":"Autoimmune Diseases and Psychotic Disorders","volume":"10","author":"Jeppesen","year":"2019","journal-title":"Front. Psychiatry"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1300\/J115v26n01_05","article-title":"Lactmed: New NLM database on drugs and lactation","volume":"26","author":"Tomasulo","year":"2007","journal-title":"Med. Ref. Serv. Q."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/asi.4630320605","article-title":"TOXLINE: Evolution of an online interactive bibliographic database","volume":"32","author":"Schultheisz","year":"1981","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_58","unstructured":"Cameron, P.T., Stump, J.M., and Schofield, L. (1986). Chemical Carcinogenesis Research Information System (Ccris) Data Bank."},{"key":"ref_59","first-page":"1","article-title":"Survey of Machine Learning Algorithms for Disease Diagnostic","volume":"9","author":"Fatima","year":"2017","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yu, J., Hu, Y., Xu, Y., Wang, J., Kuang, J., Zhang, W., Shao, J., Guo, D., and Wang, Y. (2019). LUAD pp: An effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features. BMC Cancer, 19.","DOI":"10.1186\/s12885-019-5433-7"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hepler, N.L., Scheffler, K., Weaver, S., Murrell, B., Richman, D.D., Burton, D.R., Poignard, P., Smith, D.M., and Kosakovsky Pond, S.L. (2014). IDEPI: Rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform. PLoS Comput. Biol., 10.","DOI":"10.1371\/journal.pcbi.1003842"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1080\/1744666X.2019.1623670","article-title":"Artificial intelligence and immunotherapy","volume":"15","author":"Jabbari","year":"2019","journal-title":"Expert Rev. Clin. Immunol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.cmpb.2017.10.011","article-title":"Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design","volume":"153","author":"Moghram","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yaniv, Z., Faruque, J., Howe, S., Dunn, K., Sharlip, D., Bond, A., Perillan, P., Bodenreider, O., Ackerman, M.J., and Yoo, T.S. (2016, January 18\u201320). The national library of medicine pill image recognition challenge: An initial report. Proceedings of the 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA.","DOI":"10.1109\/AIPR.2016.8010584"},{"key":"ref_65","unstructured":"Singh, A., Thakur, N., and Sharma, A. (2016, January 16\u201318). A review of supervised machine learning algorithms. Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development, New Delhi, India."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"128","DOI":"10.14445\/22312803\/IJCTT-V48P126","article-title":"Supervised Machine Learning Algorithms: Classification and Comparison","volume":"48","author":"Akinsola","year":"2017","journal-title":"Int. J. Comput. Trends Technol."},{"key":"ref_67","unstructured":"Abdellatif, A.A., Mhaisen, N., Chkirbene, Z., Mohamed, A., Erbad, A., and Guizani, M. (2021). Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Quinlan, J.R. (1987, January 22\u201325). Decision Trees as Probabilistic Classifiers. Proceedings of the Fourth International Workshop on MACHINE LEARNING, Morgan Kaufmann, MA, USA.","DOI":"10.1016\/B978-0-934613-41-5.50007-6"},{"key":"ref_69","unstructured":"Chao, Y., Liu, J., and Nemati, S. (2019). Reinforcement learning in healthcare: A survey. arXiv."},{"key":"ref_70","first-page":"1","article-title":"Introduction to semi-supervised learning","volume":"3","author":"Zhu","year":"2009","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Unsupervised Learning. The Elements of Statistical Learning, Springer Series in Statistics Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TKDE.2008.88","article-title":"Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters","volume":"20","author":"Li","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Khan, K., Rehman, S.U., Aziz, K., Fong, S., and Sarasvady, S. (2014, January 17\u201319). DBSCAN: Past, present and future. Proceedings of the Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Bengalura, India.","DOI":"10.1109\/ICADIWT.2014.6814687"},{"key":"ref_74","unstructured":"Szepesvari, C. (2010). Algorithms for Reinforcement Learning, Morgan and Claypool."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1161\/CIRCRESAHA.118.313911","article-title":"Discovery of distinct immune phenotypes using machine learning in pulmonary arterial hypertension","volume":"124","author":"Sweatt","year":"2019","journal-title":"Circ. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1080\/21645515.2019.1654807","article-title":"Combining immunoprofiling with machine learning to assess the effects of adjuvant formulation on human vaccine-induced immunity","volume":"16","author":"Chaudhury","year":"2020","journal-title":"Hum. Vaccines Immunother."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Andr\u00e9s-Rodr\u00edguez, L., Borr\u00e0s, X., Feliu-Soler, A., P\u00e9rez-Aranda, A., Rozadilla-Sacanell, A., Arranz, B., Montero-Marin, J., Garc\u00eda-Campayo, J., Angarita-Osorio, N., and Maes, M. (2020). Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20174231"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.kint.2017.01.017","article-title":"Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections","volume":"92","author":"Zhang","year":"2017","journal-title":"Kidney Int."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"17508","DOI":"10.1038\/s41598-018-35452-x","article-title":"Identification of Immune Signatures of Novel Adjuvant Formulations Using Machine Learning","volume":"8","author":"Chaudhury","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.jid.2018.09.018","article-title":"Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding\u2013Based Machine Learning Approach","volume":"139","author":"Patrick","year":"2019","journal-title":"J. Investig. Dermatol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2017.08.005","article-title":"Identification of immune correlates of protection in Shigella infection by application of machine learning","volume":"74","author":"Arevalillo","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Polano, M., Chierici, M., Dal Bo, M., Gentilini, D., Di Cintio, F., Baboci, L., Gibbs, D.L., Furlanello, C., and Toffoli, G. (2019). A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning. Cancers, 11.","DOI":"10.3390\/cancers11101562"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"594030","DOI":"10.3389\/fcimb.2020.594030","article-title":"Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis","volume":"10","author":"Meier","year":"2021","journal-title":"Front. Cell Infect. Microbiol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.cell.2018.03.034","article-title":"Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation","volume":"173","author":"Malta","year":"2018","journal-title":"Cell"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s11011-018-0339-7","article-title":"Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia","volume":"34","author":"Kanchanatawan","year":"2019","journal-title":"Metab. Brain Dis."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4326","DOI":"10.1158\/1078-0432.CCR-20-0071","article-title":"Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin\u2013Stained Sections","volume":"26","author":"Lau","year":"2020","journal-title":"Clin. Cancer Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.coisb.2020.10.010","article-title":"Mining adaptive immune receptor repertoires for biological and clinical information using machine learning","volume":"24","author":"Greiff","year":"2020","journal-title":"Curr. Opin. Syst. Biol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"110126","DOI":"10.1016\/j.jpsychores.2020.110126","article-title":"Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease","volume":"134","author":"Tennenhouse","year":"2020","journal-title":"J. Psychosom. Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"126703","DOI":"10.1016\/j.jtemb.2020.126703","article-title":"Trace element immune and opioid biomarkers of unstable angina, increased atherogenicity and insulin resistance: Results of machine learning","volume":"64","author":"Qazmooz","year":"2021","journal-title":"J. Trace Elem. Med. Biol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1038\/s41598-017-03780-z","article-title":"Image based Machine Learning for identification of macrophage subsets","volume":"7","author":"Rostam","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Konishi, H., Komura, D., Katoh, H., Atsumi, S., Koda, H., Yamamoto, A., Seto, Y., Fukayama, M., Yamaguchi, R., and Imoto, S. (2019). Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2853-y"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s00432-020-03396-3","article-title":"An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning","volume":"147","author":"Ren","year":"2021","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"e485","DOI":"10.1016\/S2665-9913(20)30168-5","article-title":"Disease-associated and patient-specific immune cell signatures in juvenile-onset systemic lupus erythematosus: Patient stratification using a machine-learning approach","volume":"2","author":"Robinson","year":"2020","journal-title":"Lancet Rheumatol."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"749","DOI":"10.4049\/jimmunol.1900033","article-title":"SIMON. an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses","volume":"203","author":"Adriana","year":"2019","journal-title":"J. Immunol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"835","DOI":"10.3389\/fonc.2020.00835","article-title":"Development and Validation of a 12-Gene Immune Relevant Prognostic Signature for Lung Adenocarcinoma Through Machine Learning Strategies","volume":"10","author":"Xue","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ejca.2019.06.020","article-title":"Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy","volume":"119","author":"Mekki","year":"2019","journal-title":"Eur. J. Cancer"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"4864","DOI":"10.1002\/cam4.3107","article-title":"Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images","volume":"9","author":"Ono","year":"2020","journal-title":"Cancer Med."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"361","DOI":"10.3389\/fimmu.2021.592303","article-title":"Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission","volume":"12","author":"Banerjee","year":"2021","journal-title":"Front. Immunol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1186\/s12967-020-02542-2","article-title":"The study of the association between immune monitoring and pneumonia in kidney transplant recipients through machine learning models","volume":"18","author":"Peng","year":"2020","journal-title":"J. Transl. Med."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Abuhelwa, A.Y., Kichenadasse, G., McKinnon, R.A., Rowland, A., Hopkins, A.M., and Sorich, M.J. (2021). Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer. Cancers, 13.","DOI":"10.3390\/cancers13092001"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4018","DOI":"10.1245\/s10434-020-09374-w","article-title":"Integrating Machine Learning and Tumor Immune Signature to Predict Oncologic Outcomes in Resected Biliary Tract Cancer","volume":"28","author":"Ji","year":"2020","journal-title":"Ann. Surg. Oncol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/hep4.1626","article-title":"Pilot Study Using Machine Learning to Identify Immune Profiles for the Prediction of Early Virological Relapse After Stopping Nucleos(t)ide Analogues in HBeAg-Negative CHB","volume":"5","author":"Lin","year":"2021","journal-title":"Hepatol. Commun."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"110136","DOI":"10.1016\/j.pnpbp.2020.110136","article-title":"A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach","volume":"105","author":"Poletti","year":"2021","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.iotech.2019.11.002","article-title":"Driving innovation for rare skin cancers: Utilizing common tumours and machine learning to predict immune checkpoint inhibitor response","volume":"4","author":"Fehrmann","year":"2019","journal-title":"Immuno-Oncol. Technol."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Awais, M., Ghayvat, H., Pandarathodiyil, A.K., Ghani, W.M.N., Ramanathan, A., Pandya, S., Walter, N., Saad, M.N., Zain, R.B., and Faye, I. (2020). Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging. Sensors, 20.","DOI":"10.3390\/s20205780"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TETC.2019.2896325","article-title":"Privacy-preserving reinforcement learning design for patient-centric dynamic treatment regimes","volume":"9","author":"Liu","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/nmeth.3707","article-title":"Deep learning","volume":"13","author":"Rusk","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1001\/jama.2018.11100","article-title":"Deep learning\u2014A technology with the potential to transform health care","volume":"320","author":"Geoffrey","year":"2018","journal-title":"JAMA"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","article-title":"Recent Progress on Generative Adversarial Networks (GANs): A Survey","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Hung, J., Goodman, A., Ravel, D., Lopes, S.C., Rangel, G.W., Nery, O.A., Malleret, B., Nosten, F., Lacerda, M.V., and Ferreira, M.U. (2020). Keras R-CNN: Library for cell detection in biological images using deep neural networks. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03635-x"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Naylor, P., Lae, M., Reyal, F., and Walter, T. (2017, January 18\u201321). Nuclei segmentation in histopathology images using deep neural networks. Proceedings of the 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), Melbourne, Australia.","DOI":"10.1109\/ISBI.2017.7950669"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1111\/1755-0998.13379","article-title":"Distinguishing between recent balancing selection and incomplete sweep using deep neural networks","volume":"21","author":"Isildak","year":"2021","journal-title":"Mol. Ecol. Resour."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isci.2019.09.018","article-title":"Deep Learning Implicitly Handles Tissue Specific Phenomena to Predict Tumor DNA Accessibility and Immune Activity","volume":"20","author":"Wnuk","year":"2019","journal-title":"iScience"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.3389\/fimmu.2019.02559","article-title":"DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity","volume":"10","author":"Wu","year":"2019","journal-title":"Front. Immunol."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"6335","DOI":"10.7717\/peerj.6335","article-title":"Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks","volume":"7","author":"Aprupe","year":"2019","journal-title":"PeerJ"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1016\/j.cell.2018.08.039","article-title":"A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging","volume":"174","author":"Keren","year":"2018","journal-title":"Cell"},{"key":"ref_119","unstructured":"Widrich, M., Sch\u00e4fl, B., Ramsauer, H., Pavlovi\u0107, M., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G.K., Greiff, V., and Hochreiter, S. (2007). Modern hopfield networks and attention for immune repertoire classification. arXiv."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"bbab160","DOI":"10.1093\/bib\/bbab160","article-title":"DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity","volume":"22","author":"Li","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Han, Y., and Kim, D. (2017). Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1997-x"},{"key":"ref_122","first-page":"1","article-title":"CP-BDHCA: Blockchain-based Confidentiality-Privacy preserving Big Data scheme for healthcare clouds and applications","volume":"25","author":"Ghayvat","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Ferri-Borgogno, S., Jianting, Y., Tsz-Lun, B., and Jared, C. (2020). Deep learning on image-omics data in identifying prognostic immune biomarkers for ovarian cancer. Res. Sq.","DOI":"10.21203\/rs.3.rs-67036\/v1"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"118248","DOI":"10.1016\/j.lfs.2020.118248","article-title":"Evaluation of immune infiltrating of thyroid cancer based on the intrinsic correlation between pair-wise immune genes","volume":"259","author":"Jia","year":"2020","journal-title":"Life Sci."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"101065","DOI":"10.1016\/j.tranon.2021.101065","article-title":"Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients","volume":"14","author":"Li","year":"2021","journal-title":"Transl. Oncol."},{"key":"ref_126","first-page":"1834","article-title":"Tumor infiltrating lymphocyte clusters are associated with response to immune checkpoint inhibition in BRAF V600E\/K mutated malignant melanomas","volume":"11","author":"Klein","year":"2021","journal-title":"Nature"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1097\/MIB.0000000000001222","article-title":"Machine Learning\u2013Based Gene Prioritization Identifies Novel Candidate Risk Genes for Inflammatory Bowel Disease","volume":"23","author":"Isakov","year":"2017","journal-title":"Inflamm. Bowel Dis."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"648329","DOI":"10.3389\/fgene.2021.648329","article-title":"Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis","volume":"12","author":"Ning","year":"2021","journal-title":"Front. Genet."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.7150\/thno.48027","article-title":"Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images","volume":"11","author":"Turkki","year":"2021","journal-title":"Theranostics"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Mallio, C.A., Napolitano, A., Castiello, G., Giordano, F.M., D\u2019Alessio, P., Iozzino, M., Sun, Y., Angeletti, S., Russano, M., and Santini, D. (2021). Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis. Cancers, 13.","DOI":"10.3390\/cancers13040652"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"38","DOI":"10.4103\/2153-3539.189703","article-title":"Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples","volume":"7","author":"Turkki","year":"2016","journal-title":"J. Pathol. Inform."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"10838","DOI":"10.7150\/thno.50283","article-title":"Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma","volume":"10","author":"Park","year":"2020","journal-title":"Theranostics"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"642167","DOI":"10.3389\/fimmu.2021.642167","article-title":"Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients","volume":"12","author":"Huang","year":"2021","journal-title":"Front. Immunol."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, J., Yao, J., Wei, M., Han, W., Chen, J., and Sun, L. (2021). Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors, 21.","DOI":"10.3390\/s21020512"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41523-020-00205-5","article-title":"Unmasking the immune microecology of ductal carcinoma in situ with deep learning","volume":"7","author":"Narayanan","year":"2021","journal-title":"NPJ Breast Cancer"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Hussain, S., Das, A., Nguyen, B.P., Marzuki, M., Lin, S., Kumar, A., Wright, G., and Singhal, A. (2019, January 17\u201320). DeLHCA: Deep transfer learning for high-content analysis of the effects of drugs on immune cells. Proceedings of the TENCON 2019-2019 IEEE Region 10 Conference (TENCON), Kochi, India.","DOI":"10.1109\/TENCON.2019.8929476"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"59353","DOI":"10.1109\/ACCESS.2021.3073408","article-title":"Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review","volume":"9","author":"Mishra","year":"2021","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/7786\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:34:42Z","timestamp":1760168082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/23\/7786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,23]]},"references-count":137,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21237786"],"URL":"https:\/\/doi.org\/10.3390\/s21237786","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,23]]}}}