Data integration in the era of omics: current and future challenges

By David Gomez-Cabrero, I. Abugessaisa, D. Maier, Andrew E. Teschendorff, Matthias Merkenschlager, A. Gisel, Esteban Ballestar, E. Bongcam-Rudloff, Ana Conesa, Jesper N. Tegnér. BMC Systems Biology, 2014

379 citations recorded by the source corpus.

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f018f775d72bd86200cba2e623ae84449147f49f

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In Molecular Biology

1. Michelle Turcotte
2. D. Meyre
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Citations in the corpus

• From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “…and Facebook to tackle the fairly homogenous big data (i.e. user data) [37], the heterogeneous nature of omics data presents a new challenge that requires sufficient understanding of the underlying biological concepts and analysis algorithms to carry out data integration and interpretation [38].”