Benutzer: Gast  Login
Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
BayBioMS; classification; competition binding; kinobeads; labeling; machine learning; proteomics
Zeitschriftentitel:
Journal of Proteome Research
Jahr:
2019
Band / Volume:
18
Heft / Issue:
4
Seitenangaben Beitrag:
1486-1493
Volltext / DOI:
doi:10.1021/acs.jproteome.8b00724
Verlag / Institution:
American Chemical Society (ACS)
E-ISSN:
1535-38931535-3907
Publikationsdatum:
25.02.2019
 BibTeX