@inproceedings{aff2b14070d2466d8d2d8537a5ce1700,
title = "Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features",
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 timeconsuming 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",
author = "Wang, {Chia Chi} and Lin, {Ying Chi} and Lin, {Yuan Chung} and Jhang, {Syu Ruei} and Tung, {Chun Wei}",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-31744-1_26",
language = "English",
isbn = "9783319317434",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "293--303",
editor = "Francisco Ortuno and Ignacio Rojas",
booktitle = "Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings",
note = "4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 ; Conference date: 20-04-2016 Through 22-04-2016",
}