Abstract
Nowadays, recent technologies in biology has gained a lot of attention, because of the massive data they produced with different types, very complex structures and various interaction categories. They allowed to perform deep analysis on cell structure and it’s sub-system. Moreover, They enabled construction of complex networks that represent the extracted data and the mutual interactions between biological entities of diverse types. However, most of users, especially researchers and biologists, find it difficult to do their experiments on a set of data of various types stored in multiple databases. In this paper, we present the state of the art for data integration based on collective mining, using various types of networked biological data. Moreover, we propose a new approach to make it possible to integrate heterogeneous data in the MicroCancer platform, recently developed by our laboratory, to deal with micro-array data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gligorijevic, V., Przulj, N.: Methods for biological data integration: perspectives and challenges. J. R. Soc. Interface 12, 20150571 (2015)
Hawkins, R., Hon, G., Ren, B.: Next-generation genomics: an integrative approach. Nat. Rev. Genet. 11, 476–486 (2010)
Nielsen, R., Paul, J., Albrechtsen, A., Song, Y.: Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 12, 443–451 (2011)
Hirschhorn, J., Daly, M.: Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005)
Duerr, R., et al.: A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 314, 1461–1463 (2006)
Quackenbush, J.: Computational analysis of microarray data. Nat. Rev. Genet. 2, 418–427 (2001)
Dahlquist, K., Salomonis, N., Vranizan, K., Lawlor, S., Conklin, B.: GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31, 19–20 (2002)
Marioni, J., Mason, C., Mane, S., Stephens, M., Gilad, Y.: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008)
Mortazavi, A., Williams, B., McCue, K., Schaeffer, L., Wold, B.: Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5(7), 621–628 (2008)
Wang, Z., Gerstein, M., Snyder, M.: RNA-seq: are volutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009)
West, D.: Introduction to Graph Theory, 2nd edn. Prentice Hall, New York, NY (2000)
Newman, M.: Networks: An Introduction. Oxford University Press, Inc., New York, NY (2010)
Fadoua, R.: Techniques de data mining pour la prise de décision sur les données despuces à ADN. Ph.D. dissertation. Department Informatique, UAE University (2017)
Rafii, F., Hassani, B.D.R., Kbir, M.A.: New approach for microarray data decision making with respect to multiple source. In: Proceeding BDCA’17 Proceedings of the 2nd International Conference on Big Data, Cloud and Applications. Article No. 106, 29–30 Mar 2017. Tetouan, Morocco (2016)
Rafii, F., Hassani, B.D.R., Kbir, M.A.: Exploring semantic web technologies to integrate microarray experiments for cancer studies. Int. J. Emerg. Trends Eng. Dev. 6(6), 251–265 (2016)
Rafii, F., Kbir, M.A. Hassani, B.D.R.; Microarray data integration to explore the wealth of sources generated by modern molecular biology. In: Veille Stratégique Scientifique et Technologique, pp. 11–13. Granada, Spain (2015)
Rafii, F., Kbir M.A., Hassani, B.D.R.: Microarray data integration for efficient decision making. In: Conférence sur les Avancées des Systèmes Décisionnels, pp. 10–12. Tangier, Morocco (2015)
Rafii, F., Hassani, B.D.R., Kbir, M.A.: Lung cancer diagnosis based on microarray data by using ART2 network. Int. J. Comput. Sci. Trends Technol. 4(3), 129–136 (2016)
Rafii, F., Hassani, B.D.R., Kbir, M.A.: Automatic clustering of microarray data using ART2 neural network. J. Theoret. Appl. Inf. Technol 90(1), 175–184 (2016)
Rafii, F. Hassani, B.D.R., Kbir, M.A.: MLP network for lung cancer presence prediction based on microarray data. In: Third World Conference on Complex Systems, pp. 23–25. IEEE, Marrakech, Morocco (2015)
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
Hanafi, H., Rafii, F., Hassani, B.D.R., Kbir, M.A. (2019). Integration Methods for Biological Data Sources. In: Ben Ahmed, M., Boudhir, A., Younes, A. (eds) Innovations in Smart Cities Applications Edition 2. SCA 2018. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-11196-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-11196-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11195-3
Online ISBN: 978-3-030-11196-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)