Benchmarking AI Workflows for Hit Detection in High-Content Screening

Published: 15 September 2022| Version 1 | DOI: 10.17632/hctkwwzx5z.1
Contributor:
Erwin Kupczyk

Description

The data set contains Python scripts, raw and processed data to benchmark hit detection in high-content screening. The raw data contains the extracted numerical features from high content images of 641 validated, highly selective pharmaceutically relevant inhibitors for over 123 targets. There were 979 features extracted using Perkin Elmer software Columbus (2.9.1532). In addition, two further raw data sets are available that were used to validate the machine learning models. These contain staurosporines in different concentrations or compounds that effect the cell cycle. The processed data were cleaned using an outlier test and Z-score transformed. The processing of the data is also available as a Python script and can be followed there. The other Python scripts contain the benchmarking of AI workflows for hit detection. Various machine learning models were tested, ranging from classic classifiers from the Scikit-Learn library over a partial least square regression to simple neural networks. To deal with unknown patterns in the hit detection, novelty detection was also tested. In addition, the used packages or environments with which the benchmarking was carried out are included.

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Categories

Machine Learning, Deep Learning, Classification (Machine Learning), High-Content Screening

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