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

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 set consisting of 557 166 protein/drug binding
curves achieving an AUC of 0.9987. By applying our classifier to the
data, only 6% of the data might require manual investigation. CiRCus
bundles two applications, a minimal interface to label a training
data set (CindeR) and an interface for the generation of random forest
classifiers with optional optimization of pretrained models (CurveClassification).
CiRCus is available on https://github.com/kusterlab accompanied by an in-depth user manual and video tutorial.