Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features

Chia Chi Wang; Ying Chi Lin; Yuan Chung Lin; Syu Ruei Jhang; Chun Wei Tung

doi:10.1007/978-3-319-31744-1_26

Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features

Chia Chi Wang, Ying Chi Lin, Yuan Chung Lin, Syu Ruei Jhang, Chun Wei Tung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings
Editors	Francisco Ortuno, Ignacio Rojas
Publisher	Springer Verlag
Pages	293-303
Number of pages	11
ISBN (Print)	9783319317434
DOIs	https://doi.org/10.1007/978-3-319-31744-1_26
Publication status	Published - Jan 1 2016
Externally published	Yes
Event	4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 - Granada, Spain Duration: Apr 20 2016 → Apr 22 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9656
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016
Country/Territory	Spain
City	Granada
Period	4/20/16 → 4/22/16

Keywords

Engine exhaust
Genotoxicity
Immunotoxicity
Principal component regression
Proinflammatory potential

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-31744-1_26

Cite this

Wang, C. C., Lin, Y. C., Lin, Y. C., Jhang, S. R., & Tung, C. W. (2016). Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features. In F. Ortuno, & I. Rojas (Eds.), Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings (pp. 293-303). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9656). Springer Verlag. https://doi.org/10.1007/978-3-319-31744-1_26

Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features. / Wang, Chia Chi; Lin, Ying Chi; Lin, Yuan Chung et al.

Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. ed. / Francisco Ortuno; Ignacio Rojas. Springer Verlag, 2016. p. 293-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9656).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 F Ortuno & I Rojas (eds), Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9656, Springer Verlag, pp. 293-303, 4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016, Granada, Spain, 4/20/16. https://doi.org/10.1007/978-3-319-31744-1_26

Wang CC, Lin YC, Lin YC, Jhang SR, Tung CW. Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features. In Ortuno F, Rojas I, editors, Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. Springer Verlag. 2016. p. 293-303. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-31744-1_26

Wang, Chia Chi ; Lin, Ying Chi ; Lin, Yuan Chung et al. / Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features. Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. editor / Francisco Ortuno ; Ignacio Rojas. Springer Verlag, 2016. pp. 293-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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AB - 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.

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Prediction of proinflammatory potentials of engine exhausts by integrating chemical and biological features

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Keywords

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