Abstract
Life science research aims at understanding the relationships in genomics, proteomics and metabolomics on all levels of biological self organization, dealing with data of increasing dimension and complexity. Bioimages represent a new data domain in this context, gaining growing attention since it closes important gaps left by the established molecular techniques. We present a new, web-based strategy that allows a new way of collaborative bioimage interpretaion through knowledge integration. We show, how this can be supported by combining data mining algorithms running on powerful compute servers and a next generation rich internet application (RIA) front-end offering database/project management and high-level tools for exploratory data analysis and annotation. We demonstrate our system BioIMAX using a bioimage dataset from High-Content Screening experiments to study bacterial infection in cell cultures.
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Loyek, C., Kölling, J., Langenkämper, D., Niehaus, K., Nattkemper, T.W. (2011). A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_25
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DOI: https://doi.org/10.1007/978-3-642-24800-9_25
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