Chemical Recognition using the Time-dependent Cellular Response Profiles^*

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• 作者：Jiao Chen^* ; ^** ; ^{luckycj79@163.com" class="auth_mail" title="E-mail the corresponding author} ; Tianhong Pan^* ; ^{thpan@ujs.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Haoran Li^* ; ^{lihaoran32@qq.com" class="auth_mail" title="E-mail the corresponding author} ; ^{luckycj79@163.com" class="auth_mail" title="E-mail the corresponding author} ; Kaili Xu^* ; ^{kaili_xu@foxmail.com" class="auth_mail" title="E-mail the corresponding author} ; Zhengming Li^* ; ^{lzming@ujs.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：TCRPs ; Cytotoxicity ; Real time cellular analyzer high throughput (TRCA HT) system ; Singular Value Decomposition(SVD) ; k-means cluster
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：7
• 页码：195-199
• 全文大小：377 K

文摘

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.