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Descriptor selection improvements for quantitative structure-activity relationships
Citation
Xia, L-Y and Wang, Q-Y and Cao, Z and Liang, Y, Descriptor selection improvements for quantitative structure-activity relationships, International Journal of Neural Systems, 29, (9) pp. 1950016. ISSN 1793-6462 (2019) [Refereed Article]
Copyright Statement
Copyright 2019 World Scientific Publishing Company
DOI: doi:10.1142/S0129065719500163
Abstract
Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid the over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and P-values.
Item Details
Item Type: | Refereed Article |
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Keywords: | quantitative structure-activity, QSAR, biological activity, descriptor selection, SPL, Logsum penalized LR |
Research Division: | Information and Computing Sciences |
Research Group: | Machine learning |
Research Field: | Neural networks |
Objective Division: | Defence |
Objective Group: | Defence |
Objective Field: | Intelligence, surveillance and space |
UTAS Author: | Cao, Z (Dr Zehong Cao) |
ID Code: | 132868 |
Year Published: | 2019 |
Web of Science® Times Cited: | 3 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2019-05-23 |
Last Modified: | 2020-08-17 |
Downloads: | 0 |
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