Computational Methods for Integration
of Biological Data
Vladimir Gligorijevic ́and NatasˇaPržulj
Abstract As we keep accumulating large sets of diverse biological data, there is a
need for efficient extraction of biological knowledge from the data. Developing
computational tools for efficient integration of biological data, obtained from
diverse experiments, has recently gained attention. Previous computational tools
are mainly designed to analyze only one particular type of the biological data. Data
of one type provide an incomplete and often obscure picture of cellular functioning.
Analysis of these data separately can only partially address important biological
questions, such as emergence of diseases and the development of novel diagnostic
and therapeutic approaches. Therefore, a key strategy for deeper understanding of
the functioning of a cell and better understanding of the molecular bases of human
diseases is data integration. Here, we classify current integrative approaches and
review their applications in addressing fundamental biological questions that can
increase our understanding of a biological system.
1 Introduction
Integration of biological data from diverse experiments to gain deeper insight into
the functioning of a cell and to better understand the molecular bases of human
diseases has become a focus of bioinformatics research. Rapid advances of tech-
niques in experimental biology have led to the production of vast amounts of
diverse biological data. Such techniques have been developed to examine various
aspects of molecular interactions and processes within a cell. For example, high-
throughput methods, such as yeast two-hybrid (Y2H) assays,^1 which explore the
binary interaction among two proteins at a time, and affinity purification with mass
Vladimir Gligorijevic ́, Ph.D. Department of Computing, Imperial College London, UK.
Professor NatasˇaPržulj, Ph.D., Department of Computing, University College London, UK.
(^1) Ito et al. ( 2000 ), Uetz et al. ( 2000 ), Giot et al. ( 2003 ), Li et al. ( 2004 ), Stelzl et al. ( 2005 ), Simonis
et al. ( 2009 ), and Consortium AIM ( 2011 ).
V. Gligorijevic ́, Ph.D. • N. Pržulj, Ph.D. (*)
Department of Computing, Imperial College London, London, UK
e-mail:[email protected];[email protected]
©Springer International Publishing Switzerland 2016
N. Bodiroga-Vukobrat et al. (eds.),Personalized Medicine,Europeanization and
Globalization 2, DOI 10.1007/978-3-319-39349-0_8
137