Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

• Published in: BMC genomics 2014-03, Vol.15 (1), p.248-248, Article 248
• Main Authors: Wei, Xiaomou, Ai, Junmei, Deng, Youping, Guan, Xin, Johnson, David R, Ang, Choo Y, Zhang, Chaoyang, Perkins, Edward J
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Biomarkers; Cancer; Chemicals; Cluster Analysis; Comparative analysis; Computational Biology; Data analysis; Ecotoxicology - methods; Ecotoxicology - standards; Engineers; Experiments; Gene expression; Gene Expression Profiling; Gene Expression Regulation - drug effects; Gene Regulatory Networks; Genetic engineering; Hazardous Substances - classification; Hazardous Substances - toxicity; Machine learning; Male; Metabolic Networks and Pathways; Models, Statistical; Principal components analysis; Rats; Reproducibility of Results; Rodents; Sample size; Signal Transduction; Studies; Support Vector Machine; Toxicity; Transcriptome
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-15-248

High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.