The Role of Computer Science based on Industry 5.0 in Bioinformatics
Keywords:
Computer algorithms, Industry 5.0, bioinformatics, computer scientists, big dataAbstract
The term "Industry 5.0" was created to address personalized production and the empowerment of humans in manufacturing processes, as Industry 4.0 was unable to meet the increasing need for customization. There are differing opinions about what Industry 5.0 is and what comprises the reconciliation of humans and robots from the term's inception. This provides the driving force behind this paper's identification and analysis of the numerous topics and research trends surrounding Industry 5.0's use of text mining tools and methodologies. The purpose of this paper is to familiarize computer science with the emerging discipline of bioinformatics. The requirement for biologists to make use of and contribute to the interpretation of the enormous volumes of data generated by genomic research—and its more contemporary offshoots, proteomics, and functional genomics—has given rise to this field. The article offers a broad overview of the fundamental ideas in molecular cell biology, defines the types of computer algorithms and methodologies required to comprehend cell behavior, and describes the nature of the data that is now available. There is a lot of overlap regarding the function of computer science despite these distinctions. In addition to comparing different strategies, this research highlights some of the inherent difficulties and explores how computer science has been included in these two undergraduate bioinformatics programs.
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Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., & Edinbarough, I. (2022). State of Industry 5.0—Analysis and identification of current research trends. Applied System Innovation, 5(1), 27.
Burhans, D. T., & Skuse, G. R. (2004). The role of computer science in undergraduate bioinformatics education. ACM SIGCSE Bulletin, 36(1), 417-421.
Dubay, C., Brundege, J. M., Hersh, W., & Spackman, K. (2002). Delivering bioinformatics training: bridging the gaps between computer science and biomedicine. In Proceedings of the AMIA Symposium (p. 220). American Medical Informatics Association.
Mathur, M. (2018). Bioinformatics challenges: a review. Bioinformatics, 3(6).
Payne, P. R., Bernstam, E. V., & Starren, J. B. (2018). Biomedical informatics meets data science: current state and future directions for interaction. JAMIA open, 1(2), 136-141.
Dutta-Moscato, J., Gopalakrishnan, V., Lotze, M. T., & Becich, M. J. (2014). Creating a pipeline of talent for informatics: STEM initiative for high school students in computer science, biology, and biomedical informatics. Journal of pathology informatics, 5(1), 12.
Beretta, S., Cannataro, M., & Castelli, M. (2016). 9th workshop on biomedical and bioinformatics challenges for computer science-BBC2016. Procedia Computer Science, 80, 962-964.
Beretta, S., Cannataro, M., & Dondi, R. (2015). 8th Workshop on Biomedical and Bioinformatics Challenges for Computer Science–BBC2015. Procedia Computer Science, 51, 680-682.
Agapito, G., Cannataro, M., Castelli, M., Dondi, R., & Zoppis, I. (2017). 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science-BBC2017. Procedia Computer Science, 108, 1113-1114.
Nayak, J., Naik, B., Dinesh, P., Vakula, K., & Dash, P. B. (2020). Firefly algorithm in biomedical and health care: advances, issues and challenges. SN Computer Science, 1(6), 311.
Cohen, J. (2004). Bioinformatics—an introduction for computer scientists. ACM Computing Surveys (CSUR), 36(2), 122-158.
Gauthier, J., Vincent, A. T., Charette, S. J., & Derome, N. (2019). A brief history of bioinformatics. Briefings in bioinformatics, 20(6), 1981-1996.
Nagaraj, K., Sharvani, G. S., & Sridhar, A. (2018). Emerging trend of big data analytics in bioinformatics: a literature review. International Journal of Bioinformatics Research and Applications, 14(1-2), 144-205.
Muruganandam, S., Salameh, A. A., Pozin, M. A. A., Manikanthan, S. V., & Padmapriya, T. (2023). Sensors and machine learning and AI operation-constrained process control method for sensor-aided industrial internet of things and smart factories. Measurement: Sensors, 25, 100668.
Sun, H. (2022). Construction of Computer Algorithms in Bioinformatics of the Fusion Genetic Algorithm. Mathematical Problems in Engineering.
Lv, Y., Huang, S., Zhang, T., & Gao, B. (2021). Application of multilayer network models in bioinformatics. Frontiers in Genetics, 12, 664860.
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