Data integration: towards understanding biological complexity

by D. Gomez-Cabrero, J. Tegner


Data integration: towards understanding biological complexity
D. Gomez-Cabrero, J. Tegner
Chapter 4:. Handbook of Statistical Systems Biology. Editors Stumpf, Balding and Girolami. John Wiley and Sons, 2011


​Systems biology will fully develop its potential when researchers are able to make use of all the available data. To fulfill this goal it is needed to overcome two major challenges: how to store and make data and knowledge accessible; and how to integrate and analyze data from different sources and of different types. Hence, this chapter is divided into two sections. The first section describes public storage resources such as experimental data repositories, ontologies and knowledge databases; however we will not discuss the (relevant topic of) requirements for a data-warehouse capable of managing several databases in an inter-operable manner (Kimball and Ross 2002). The second section reviews available tools and algorithms that are useful for integrating different types of data sets. However, since a comprehensive enumeration is beyond the scope of this chapter, we present some representative examples.
In this chapter we list integrative tools using open source codes and public repositories and databases. The relevant R packages (Ihaka and Gentleman 1996) are highlighted in bold.

DOI: 10.1002/9781119970606.ch4

Data integration - towards understanding biological complexity.pdf


Data integration Biological entities Knowledge Databases and Ontologies Open Biomedical Ontologies (OBOs) KDs and Ontologies Clustering algorithms Gene-set analysis

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