Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
710355 | IFAC-PapersOnLine | 2016 | 5 Pages |
Abstract
To distinguish the chemicals on the cellular level, a pattern recognition approach, which uses the time-dependent cellular response profiles (TCRPs), is proposed in this paper. Firstly, the TCRPs is collected from the xCELLigence real time cellular analyzer high throughput (RTCA HT) system. Secondly, based on the traditional cellular toxic-effect evaluation, the dose-response curves is generated from the multi-concentration TCRPs. And then features are extracted from the produced dose-response curves. Thirdly, an improved k-means cluster is used to classify the extracted features. The proposed method can provide a useful solution and a high throughput screening for chemical recognition at the cellular level.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Jiao Chen, Tianhong Pan, Haoran Li, Kaili Xu, Zhengming Li,