We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Identification of informative features for predicting proinflammatory potentials of engine exhausts.
- Authors
Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei
- Abstract
<bold>Background: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
EMISSIONS (Air pollution); WASTE gases; PHYSIOLOGICAL effects of pollution; IMMUNOTOXICOLOGY; ALTERNATIVE fuels; AIR pollution; ALGORITHMS; INFLAMMATION; SAFETY; TOXINS; BIOINFORMATICS
- Publication
BioMedical Engineering OnLine, 2017, Vol 16, p1
- ISSN
1475-925X
- Publication type
journal article
- DOI
10.1186/s12938-017-0355-6