A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach
A robust screening approach and a sparse quantitative structure–retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed...
Saved in:
Main Authors: | Al Fakih, A. M., Algamal, Z. Y., Lee, M. H., Aziz, M. |
---|---|
格式: | Article |
出版: |
Taylor and Francis Ltd.
2017
|
主題: | |
在線閱讀: | http://eprints.utm.my/id/eprint/75754/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030458483&doi=10.1080%2f1062936X.2017.1375010&partnerID=40&md5=47d22807f6a4795a52fa1244310bb90b |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
相似書籍
-
Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm
由: Alharthi, Aiedh Mrisi, et al.
出版: (2023) -
Quantitative structure–activity relationship model for prediction study of corrosion inhibition efficiency using two-stage sparse multiple linear regression
由: Al-Fakih, A. M., et al.
出版: (2016) -
Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a quantitative structure-retention relationship (QSRR) approach
由: Akbar, Jamshed, et al.
出版: (2012) -
High-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty
由: Algamal, Z. Y., et al.
出版: (2017) -
High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm
由: Algamal, Z. Y., et al.
出版: (2016)