Abstract
In the past years, researchers and analysts worldwide determine big data as a revolution in scientific research and one of the most promising trends that has given impetus to the intensive development of methods and technologies for their investigation and has resulted in the emergence of a new paradigm for scientific research Data-Intensive Scientific Discovery (DISD). The paper presents a platform for adaptive knowledge discovery and decision making tailored to the target of scientific research. The major advantage is the automatic generation of hypotheses and options for decisions, as well as verification and validation utilizing standard data sets and expertise of scientists. The platform is implemented on the basis of scalable framework and scientific portal to access the knowledge base and the software tools, as well as opportunities to share knowledge and technology transfer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, P., Zhang, C.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. J. Inf. Sci. 275, 314–347 (2014). https://doi.org/10.1016/j.ins.2014.01.015
Roy, A.K.: Trends in computational biology and bioinformatics in the era of big data analytics. In: Conference International Workshop on Bioinformatics in Fisheries and Aquaculture’ held at ICAR- CIFRI (2017). https://doi.org/10.13140/rg.2.2.21016.39680
Thiam Yui, C., Liang, L.J., Jik Soon, W., Husain, W.: A survey on data integration in bioinformatics. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds.) ICIEIS 2011. CCIS, vol. 254, pp. 16–28. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25483-3_2
Nguyen, H., Michel, L., Thompson, J.D., Poch, O.: Heterogeneous biological data integration with declarative query language. IBM J. Res. Dev. 58(2/3), 1–12 (2014)
Rao, C.S., Somayajulu, D.V.L.N., Banka, H., Ro, S.: Feature binding technique for integration of biological databases with optimized search and retrieve. In: 2nd International Conference on Communication, Computing & Security [ICCCS-2012], pp. 622–629 (2012)
Paton, N.W., Missier, P., Hedeler, C. (eds.): DILS 2009. LNCS, vol. 5647. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02879-3
Zhang, Z., Bajic, V.B., Yu, J., Cheung, K.-H., Townsend, J.P.: Data integration in bioinformatics: current efforts and challenges. In: Bioinformatics - Trends and Methodologies. pp. 41–56 (2011). ISBN 978-953-307-282-1. http://doi.org/10.5772/21654
Borovska, P., Ivanova, D.: Intelligent method for adaptive in silico knowledge discovery based on big genomic data analytics. In: AIP Conference Proceedings, vol. 2048, p. 060001 (2018). https://doi.org/10.1063/1.5082116
Ivanova, D., Borovska, P., Gancheva, V.: Experimental investigation of enhancer-promoter interactions out of genomic big data based on machine learning. Int. J. Comput. 3, 58–62 (2018). ISSN: 2367-8895
Borovska, P., Gancheva, V., Landzhev, N.: Massively parallel algorithm for multiple biological sequences alignment. In: Proceedings of the IEEE International Conference on Telecommunications and Signal Processing (TSP), Rome, Italy, pp. 638–642. ISBN 978-1-4799-0402-0
Borovska, P., Gancheva, V.: Parallelization and optimization of multiple biological sequence alignment software based on social behavior model. Int. J. Comput. 3, 69–74 (2018). ISSN: 2367-8895
Acknowledgment
This paper presents the outcomes of research project “Intelligent Method for Adaptive In-silico Knowledge Discovery and Decision Making Based on Analysis of Big Data Streams for Scientific Research”, contract DN07/24, financed by the National Science Fund, Competition for Financial Support for Fundamental Research, Ministry of Education and Science, Bulgaria.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Borovska, P., Gancheva, V., Georgiev, I. (2019). Platform for Adaptive Knowledge Discovery and Decision Making Based on Big Genomics Data Analytics. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-17935-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17934-2
Online ISBN: 978-3-030-17935-9
eBook Packages: Computer ScienceComputer Science (R0)