Abstract
With more than 200 different types of “-omic” data [1] spanning from submolecular, through molecular, cell, cell-systems, tissues, organs, phenotypes, gene-environment interactions, and ending at ecology and organism communities, the problem and complexity of bioscientific data processing has never been greater. Often data are generated in high-throughput studies with the aim to have a sufficient volume to find patterns and detect rare events. For these highthroughput approaches new methods have to be developed in order to assure integrity of the volume of data that is produced. At the same time efforts to integrate these widely-varying data types are underway in research fields such as systems biology. Systems-level research requires yet additional methodologies to pipeline, process, query, and interpret data, and such pipelines are, themselves, objects of scientific value if they can be re-used or re-purposed by other researchers.
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Kok, J., Lamprecht, AL., Verbeek, F.J., Wilkinson, M.D. (2012). Bioscientific Data Processing and Modeling. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies. ISoLA 2012. Lecture Notes in Computer Science, vol 7610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34032-1_2
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DOI: https://doi.org/10.1007/978-3-642-34032-1_2
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