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Prediction of Proinflammatory Potentials of Engine Exhausts by Integrating Chemical and Biological Features

Part of the Lecture Notes in Computer Science book series (LNBI,volume 9656)

Abstract

The increasing prevalence of immune-related diseases has raised concerns about immunotoxicity of engine exhausts. The evaluation of immunotoxicity associated with engine exhausts has relied on expensive and time-consuming experiments. In this study, a computational method named CBM was developed for predicting proinflammatory potentials of engine exhausts using chemical and biological data which are routinely analyzed for toxicity evaluation. The CBM model, based on a principal component regression algorithm, performs well with high correlation coefficient values of 0.972 and 0.849 obtained from training and independent test sets, respectively. In contrast, chemical or biological features alone showed poor correlation with the toxicity. The model indicates the importance of the utilization of both chemical and biological features for developing an effective model. The proposed method could be further developed and applied to predict bioactivities of mixtures.

Keywords

  • Engine exhaust
  • Genotoxicity
  • Immunotoxicity
  • Principal component regression
  • Proinflammatory potential

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Acknowledgement

The authors would like to acknowledge the financial support from Ministry of Science and Technology of Taiwan (MOST104-2221-E-037-001-MY3) and Kaohsiung Medical University Research Foundation (KMU-M104010 and NSYSUKMU104-I01-2).

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Correspondence to Chun-Wei Tung .

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Wang, CC., Lin, YC., Lin, YC., Jhang, SR., Tung, CW. (2016). Prediction of Proinflammatory Potentials of Engine Exhausts by Integrating Chemical and Biological Features. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_26

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