Development of predicitve models to distinguish metals from non-metal toxicants, and individual metal from one another
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RESEARCH
Open Access
Development of predicitve models to distinguish metals from non-metal toxicants, and individual metal from one another Zongtao Yu1,2,3†, Yuanyuan Fu4†, Junmei Ai3, Jicai Zhang2, Gang Huang5* and Youping Deng4* From The 20th International Conference on Bioinformatics & Computational Biology (BIOCOMP 2019) Las Vegas, NV, USA. 29 July-01 August 2019
* Correspondence: huangg@sumhs. edu.cn; [email protected] † Zongtao Yu and Yuanyuan Fu contributed equally to this work. 5 Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China 4 Bioinformatics Core, Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA Full list of author information is available at the end of the article
Abstract Background: Evaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and furthermore helping understand underlying toxic mechanisms. Results: Based on an independent dataset test, using only 15 gene markers, we were able to distinguish metals from non-metal toxicants with 100% accuracy. Of these, 6 and 9 genes were commonly down- and up-regulated respectively by most of the metals. 8 out of 15 genes belong to membrane protein coding genes. Function well annotated genes in the list include ADORA2B, ARNT, S100G, and DIO3. Also, a 10-gene marker list was identified that can discriminate an individual metal from one another with 100% accuracy. We could find a specific gene marker for each metal in the 10-gene marker list. Function well annotated genes in this list include GSTM2, HSD11B, AREG, and C8B. Conclusions: Our findings suggest that using a microarray classifier analysis, not only can we create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but we can also identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals. Keywords: Biomarker, Microarray, Toxic heavy metals, Classification
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