Structure of data in cell biology research
Keywords:
molecular biology; data quality; SQL; database; SSMS.
Abstract
Bioinformatics is a scientific field on the border between informatics and biology where problems in the field of biology are solved using statistical methods. Another part of it are database systems which serve to store data necessary for meta-analysis. In recent years, there has been a boom mainly thanks to enabling technologies that make it possible to obtain big data about the functioning of living cells of organisms. Bioinformatics tools are necessary to process these data and form an integral part of research in modern biological and medical sciences. Scientific research focused on molecular biology, as well as medicine, is increasingly focusing on data storage. It is understood that the correct structure of the database is important for the correct interpretation of the results of their research activities. For communication between tables in the database, it is essential to set the data type, assign Primary key and Foreign key, ensure data integrity, remove data plurality and understand the research logic. Based on these needs, we created a relational database using SQL Server 2017 and Microsoft SQL Server Management Studio 2017 (SSMS). We created the source code for programming the database and filling it with data in Structured Query Language (SQL) and T-SQL on the Microsoft platform. Of the data types, we used float for numbers with a floating decimal line, integer values were assigned an integer (int), date had a date data type, and text strings had a defined nvarchar data type. Our results bring new information in the field of bioinformatics about the creation of a database structure for data storage in cell biology research. These new insights will help big data in meta-analyses of data and applying scientific results to medical and scientific practice. The database will store data obtained in real time, which will ensure relevance in pointing out biological trends, regularities, relationships and links between cellular structures. All these aspects are very important for the spatial modeling of data and the creation of models of interactions of cell structures with use for applications in medical and biological practice.References
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Coronel, C., & Morris, S. (2014). Database systems: Design, implementation and management. 11th ed. Cengage Learning, Stamford.
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Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Gharajeh, M. S. (2018). Biological big data analytics. Advances in Computers, 109, 321–355.
Halpin, T., & Morgan, T. (2008). Information modeling and relational databases. Elsevier Inc.
Hanine, M., Bendarag, A., & Boutkhoum, O. (2015). Data migration methodology from relational to NoSQL databases. International Journal of Computer and Information Engineering, 9(12), 2566–2570.
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90, 60–68.
Nielsen, J., & Keasling, J. D. (2016). Engineering cellular metabolism. Cell, 164(6), 1185–1197.
Ouanouki, R., April, A., Abran, A., Gomez, A., & Desharnais, J. M. (2017). Toward building RDB to Hbase conversion rules. Journal of Big Data, 4, 10.
Ragunath, P. K., Venkatesan, P., & Ravimohan, R. (2009). New curriculum design model for bioinformatics postgraduate program using systems biology approach. Journal of Computer Science and Systems Biology, 2(6), 300–305.
Raj, P. (2018). A detailed analysis of noSQL and newSQL databases for big data analytics and distributed computing. Advances in Computers, 109, 1–48.
Ramesh, S., & Henderson, A. (2018). A semantic data model: Meaning making from data structures in the SQL server. Journal of Information Systems Engineering and Business Intelligence, 4(2), 106–115.
Ramzan, S., Sarwar, I., Ramzan, B., & Anwar, W. (2019). Intelligent data engineering for migration to NoSQL based secure environments. IEEE Access, 7, 69042–69057.
Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. In: Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego. Pp. 42–47.
Samanta, A., & Chaki, N. (2023). A bi-directional engine for transformation of big data between NoSQL and SQL data.
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1), 7–33.
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers and Industrial Engineering, 115, 319–330.
Published
2023-08-20
How to Cite
Langraf, V., Svoradová, A., Petrovičová, K., & Brygadyrenko, V. (2023). Structure of data in cell biology research . Regulatory Mechanisms in Biosystems, 14(3), 492-496. https://doi.org/10.15421/10.15421/022370
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