Abstract
Recent advances of modern high-throughput technologies such as mass spectrometry and microarrays allow the measurement of cell products like proteins, peptides and mRNA under different conditions over time. Therefore, researchers have to deal with a vast amount of available measurements gained from accomplished experiments using the above techniques.
In this paper, we set our focus on methods that analyze consistency of time-resolved replicates by using similarity patterns between measured cell products over time. This fact led us to develop and evaluate a method for time points estimation of a single sample using independent replicate sets taking the existing noise in the measurements and biological perturbations into account. Moreover, the established approach can be applied to assess the preanalytical quality of biobank samples used in further biomarker research.
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Abidi, N., Klawonn, F., Thumfart, J.O. (2013). Time Point Estimation of a Single Sample from High Throughput Experiments Based on Time-Resolved Data and Robust Correlation Measures. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_4
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DOI: https://doi.org/10.1007/978-3-642-41398-8_4
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