Development of a data-processing method based on Bayesian k-means clustering to discriminate aneugens and clastogens in a high-content micronucleus assay.

Abstract:

:Genotoxicants can be identified as aneugens and clastogens through a micronucleus (MN) assay. The current high-content screening-based MN assays usually discriminate an aneugen from a clastogen based on only one parameter, such as the MN size, intensity, or morphology, which yields low accuracies (70-84%) because each of these parameters may contribute to the results. Therefore, the development of an algorithm that can synthesize high-dimensionality data to attain comparative results is important. To improve the automation and accuracy of detection using the current parameter-based mode of action (MoA), the MN MoA signatures of 20 chemicals were systematically recruited in this study to develop an algorithm. The results of the algorithm showed very good agreement (93.58%) between the prediction and reality, indicating that the proposed algorithm is a validated analytical platform for the rapid and objective acquisition of genotoxic MoA messages.

journal_name

Hum Exp Toxicol

authors

Huang ZH,Li N,Rao KF,Liu CT,Huang Y,Ma M,Wang ZJ

doi

10.1177/0960327117695635

subject

Has Abstract

pub_date

2018-03-01 00:00:00

pages

285-294

issue

3

eissn

0960-3271

issn

1477-0903

journal_volume

37

pub_type

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