Int. J. of Data Mining and Bioinformatics   »   2017 Vol.19, No.2

 

 

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.2017.10010188

 

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

 

Date of acceptance: 05 Sep 2017
Available online: 06 Jan 2018

 

 

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