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High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression

Algamal, Zakariya Yahya and Lee, Muhammad Hisyam and Al-Fakih, Abdo Mohammed (2016) High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression. Journal of Chemometrics, 30 (2). pp. 50-57. ISSN 0886-9383

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Abstract

Outliers in the biological activity variable or the heavy tailed distribution of the error are often encountered in practice. Under these circumstances, the quantittative structure-activity relationship (QSAR) model using multiple linear regression is not efficient. In this paper, a two-stage adaptive penalized rank regression is proposed for constructing a robust and efficient high-dimensional QSAR model of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors. The results demonstrate the effectiveness of our proposed method in simultaneously estimating a robust QSAR model and selecting informative molecular descriptors. Furthermore, the results prove that the proposed method can significantly encourage the grouping effect. The proposed method, because of the high predictive ability and robustness, could be a useful method in high-dimensional QSAR modeling.

Item Type:Article
Uncontrolled Keywords:Adaptive elastic net, Influenza virus inhibitors, Penalized method, QSAR, Rank regression
Subjects:Q Science > Q Science (General)
Divisions:Science
ID Code:73874
Deposited By: Fahmi Moksen
Deposited On:21 Nov 2017 03:28
Last Modified:21 Nov 2017 03:28

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