Predicting chemical bioavailability using microarray gene expression data and regression modeling: A tale of three explosive compounds

BMC Genomics

Methodology Article

Natalie D. Barker¹  Yixin Chen²  Dawn E. Wilkins²  Xiaofei Nan³  Edward J. Perkins⁴  Burton C. Suedel⁴  Ping Gong⁴  Robert E. Boyd⁴  David R. Johnson⁵ 

[1] Bennett Aerospace Inc., 27518, Cary, North Carolina, USA;Department of Computer and Information Science, University of Mississippi, 38677, Oxford, Mississippi, USA;Department of Computer and Information Science, University of Mississippi, 38677, Oxford, Mississippi, USA;Present Address: School of Information Engineering, Zhengzhou University, 450001, Zhengzhou, Henan, China;Environmental Laboratory, US Army Engineer Research and Development Center, 39180, Vicksburg, MS, USA;GHD, 75234, Dallas, Texas, USA;

关键词: Tissue residue;    Global gene expression profiling;    Predictor genes;    Predictive regression modeling;    TNT (2,4,6-Trinitrotoluene);    RDX (1,3,5-Trinitro-1,3,5-triazacyclohexane);    HMX (Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine);    Eisenia fetida;   

DOI  :  10.1186/s12864-016-2541-5

 received in 2015-03-23, accepted in 2016-02-25,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundChemical bioavailability is an important dose metric in environmental risk assessment. Although many approaches have been used to evaluate bioavailability, not a single approach is free from limitations. Previously, we developed a new genomics-based approach that integrated microarray technology and regression modeling for predicting bioavailability (tissue residue) of explosives compounds in exposed earthworms. In the present study, we further compared 18 different regression models and performed variable selection simultaneously with parameter estimation.ResultsThis refined approach was applied to both previously collected and newly acquired earthworm microarray gene expression datasets for three explosive compounds. Our results demonstrate that a prediction accuracy of R2 = 0.71–0.82 was achievable at a relatively low model complexity with as few as 3–10 predictor genes per model. These results are much more encouraging than our previous ones. ConclusionThis study has demonstrated that our approach is promising for bioavailability measurement, which warrants further studies of mixed contamination scenarios in field settings

【 授权许可】

CC BY   
© Gong et al. 2016