Identification of informative features for predicting proinflammatory potentials of engine exhausts

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

Research output: Contribution to journalArticle

Abstract

Background: The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. Methods: To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. Results: A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. Conclusions: The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

Original languageEnglish
Article number66
JournalBioMedical Engineering Online
Volume16
DOIs
Publication statusPublished - Aug 18 2017
Externally publishedYes

Fingerprint

Vehicle Emissions
Exhaust systems (engine)
Computational methods
Alternative fuels
Immune System Diseases
Bioactivity
Health
Experiments

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biomaterials
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Identification of informative features for predicting proinflammatory potentials of engine exhausts. / Wang, Chia Chi; Lin, Ying Chi; Lin, Yuan Chung; Jhang, Syu Ruei; Tung, Chun Wei.

In: BioMedical Engineering Online, Vol. 16, 66, 18.08.2017.

Research output: Contribution to journalArticle

@article{df4a0664b99d44afacd88795629194fd,
title = "Identification of informative features for predicting proinflammatory potentials of engine exhausts",
abstract = "Background: The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. Methods: To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. Results: A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. Conclusions: The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.",
author = "Wang, {Chia Chi} and Lin, {Ying Chi} and Lin, {Yuan Chung} and Jhang, {Syu Ruei} and Tung, {Chun Wei}",
year = "2017",
month = "8",
day = "18",
doi = "10.1186/s12938-017-0355-6",
language = "English",
volume = "16",
journal = "BioMedical Engineering Online",
issn = "1475-925X",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Identification of informative features for predicting proinflammatory potentials of engine exhausts

AU - Wang, Chia Chi

AU - Lin, Ying Chi

AU - Lin, Yuan Chung

AU - Jhang, Syu Ruei

AU - Tung, Chun Wei

PY - 2017/8/18

Y1 - 2017/8/18

N2 - Background: The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. Methods: To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. Results: A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. Conclusions: The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

AB - Background: The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. Methods: To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. Results: A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. Conclusions: The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

UR - http://www.scopus.com/inward/record.url?scp=85027724119&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027724119&partnerID=8YFLogxK

U2 - 10.1186/s12938-017-0355-6

DO - 10.1186/s12938-017-0355-6

M3 - Article

C2 - 28830522

AN - SCOPUS:85027724119

VL - 16

JO - BioMedical Engineering Online

JF - BioMedical Engineering Online

SN - 1475-925X

M1 - 66

ER -