https://scholars.lib.ntu.edu.tw/handle/123456789/573154
標題: | Identification of informative features for predicting proinflammatory potentials of engine exhausts | 作者: | CHIA-CHI WANG Lin Y.-C Lin Y.-C Jhang S.-R Tung C.-W. |
關鍵字: | Alternative fuels; Bioinformatics; Computational methods; Forecasting; Principal component analysis; Chemical and biologicals; Construct models; Correlation coefficient; Immunotoxicity; Prediction model; Prediction performance; Principal component regression algorithms; Proinflammatory; Exhaust systems (engine); algorithm; Ames test; Article; controlled study; correlation coefficient; engine exhaust; exhaust gas; FS CBM model; human; information processing; nonhuman; prediction; principal component regression; priority journal; process model; regression analysis; sequential backward feature elimination algorithm; biology; chemically induced; exhaust gas; inflammation; safety; toxicity; immunotoxin; Algorithms; Computational Biology; Immunotoxins; Inflammation; Safety; Vehicle Emissions | 公開日期: | 2017 | 卷: | 16 | 來源出版物: | BioMedical Engineering Online | 摘要: | 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. ? 2017 The Author(s). |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027724119&doi=10.1186%2fs12938-017-0355-6&partnerID=40&md5=d4d087159eef942b0fd543e0ebd88177 https://scholars.lib.ntu.edu.tw/handle/123456789/573154 |
ISSN: | 1475925X | DOI: | 10.1186/s12938-017-0355-6 |
顯示於: | 獸醫學系 |
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