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Title:

CiRCus: A Framework to Enable Classification of Complex High-Throughput Experiments

Document type:
Zeitschriftenaufsatz
Author(s):
Seefried, Florian; Schmidt, Tobias; Reinecke, Maria; Heinzlmeir, Stephanie; Kuster, Bernhard; Wilhelm, Mathias
Abstract:
Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying the acquired results have not kept pace, requiring significant manual efforts to do so. Here, we present CiRCus, a framework to generate custom machine learning models to classify results from high-throughput proteomics binding experiments. We show the experimental procedure that guided us to the layout of this framework as well as the usage of the framework on an example data s...     »
Keywords:
BayBioMS; classification; competition binding; kinobeads; labeling; machine learning; proteomics
Journal title:
Journal of Proteome Research
Year:
2019
Journal volume:
18
Journal issue:
4
Pages contribution:
1486-1493
Fulltext / DOI:
doi:10.1021/acs.jproteome.8b00724
Publisher:
American Chemical Society (ACS)
E-ISSN:
1535-38931535-3907
Date of publication:
25.02.2019
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