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Learning Structure and Schemas from Documents pp 413–432Cite as

A Survey on Integrating Data in Bioinformatics

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 375))

Abstract

Data integration is an open challenge in bioinformatics. Querying and retrieving data from remote and/or local sources and analyzing them are very time consuming tasks for biologists. Data integration allows biologists to combine knowledge from multiple disciplines. This has become a critical issue in biological research in recent years. Advances in technology have pathed the way to a huge and growing amount of available biological data. However, it is important to highlight that the distinctive feature in integrating biological data is not mainly concerned with the amount of data but with their complexity. Biological data sources are considered strongly heterogeneous in many aspects. Several approaches and systems based on different technologies and techniques, have been proposed in the literature to deal with the problem of integrating biological sources. Nevertheless it does not exist yet an approach able to solve all mentioned problems. This chapter provides a survey on data integration in the field of biological sources.

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Authors and Affiliations

  1. Institute for Biomedical Technologies, National Research Council, Via F.lli Cervi 93, 20090, Segrate, MI, Italy

    Andrea Manconi

  2. Dept. of Electrical and Electronic Engineering, Univ. of Cagliari, P.zza D’Armi, 09123, Cagliari, Italy

    Andrea Manconi

  3. CRS4 - Center for Advanced Studies, Research and Development in Sardinia, Loc. Piscina Manna, Ed.1, 09010, Pula, CA, Italy

    Patricia Rodriguez-Tomé

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  1. Andrea Manconi
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  1. University of New York Tirana, Rr. Komuna E Parisit,, Tirana, Albania

    Marenglen Biba

  2. Technical University of Catalonia, Campus Nord, Ed. Omega, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

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Manconi, A., Rodriguez-Tomé, P. (2011). A Survey on Integrating Data in Bioinformatics. In: Biba, M., Xhafa, F. (eds) Learning Structure and Schemas from Documents. Studies in Computational Intelligence, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22913-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-22913-8_19

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