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 journalArticlepeer-review

1 Citation (Scopus)

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

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Identification of informative features for predicting proinflammatory potentials of engine exhausts'. Together they form a unique fingerprint.

Cite this