title: Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.forename: Xiaomou
authors.initials: X
authors.lastname: Wei
authors.forename: Junmei
authors.initials: J
authors.lastname: Ai
authors.affiliation: Department of Internal Medicine, Rush University Cancer Center, Rush University Medical Center, Kidston House, 630 S, Hermitage Ave, Room 408, Chicago, IL 60612, USA. youping_deng@rush.edu.
authors.forename: Youping
authors.initials: Y
authors.lastname: Deng
authors.forename: Xin
authors.initials: X
authors.lastname: Guan
authors.forename: David R
authors.initials: DR
authors.lastname: Johnson
authors.forename: Choo Y
authors.initials: CY
authors.lastname: Ang
authors.forename: Chaoyang
authors.initials: C
authors.lastname: Zhang
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Perkins
mesh_terms: D000465:Algorithms; D000818:Animals; D015415:Biomarkers; D016000:Cluster Analysis; D019295:Computational Biology; D054750:Ecotoxicology / Q000379:methods / Q000592:standards; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000187:drug effects; D053263:Gene Regulatory Networks; D015386:Hazardous Substances / Q000145:classification / Q000633:toxicity*; D008297:Male; D053858:Metabolic Networks and Pathways; D015233:Models, Statistical; D051381:Rats; D015203:Reproducibility of Results; D015398:Signal Transduction; D060388:Support Vector Machine; D059467:Transcriptome
chemical_list: D015415:Biomarkers; D015386:Hazardous Substances