Title: Pattern recognition of chemical compounds using multiple dose-response curves

Authors: Jiao Chen; Tianhong Pan; Shan Chen; Xiaobo Zou; Kaili Xu

Addresses: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; School of Electronics and Electrical Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu 213164, China ' School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China ' School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China

Abstract: To determine distinct chemical properties characterised by Mechanism of Action (MoA), a pattern recognition algorithm using multiple dose-response curves is developed in this paper. By monitoring the dynamic time-dependent cellular response profiles (TCRPs) of living cells via Real Time Cellular Analyser, changes in cell number caused by different MoAs are recorded as a time series. Based on the toxic-effect observed in TCRPs, a dose-response curve is established, which reflect the cytotoxicity of the tested chemicals. Features, which reflect the levels of cytotoxicity, are extracted from the dose-response curves. And the singular value decomposition (SVD) is taken to reduce the effect of collinearity in the extracted features. A k-means clustering method with deterministic initial centres is employed to classify the compressed features. As a result, the tested chemicals are classified into several groups. The proposed method enables relatively high throughput screening for chemical recognition at the cellular level.

Keywords: MoA; mechanism of action; TCRP; time-dependent cell response profile; toxic-effect; k-means cluster; dose-response curve.

DOI: 10.1504/IJDMB.2017.089283

International Journal of Data Mining and Bioinformatics, 2017 Vol.19 No.2, pp.97 - 116

Received: 18 Sep 2016
Accepted: 05 Sep 2017
Published online: 11 Jan 2018 *

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