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Descriptor selection improvements for quantitative structure-activity relationships

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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 Type: | Article |
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Authors/Creators: | Xia, L-Y and Wang, Q-Y and Cao, Z and Liang, Y |
Keywords: | quantitative structure-activity, QSAR, biological activity, descriptor selection, SPL, Logsum penalized LR |
Journal or Publication Title: | International Journal of Neural Systems |
Publisher: | World Scientific Publishing Co. Pte. Ltd. |
ISSN: | 1793-6462 |
DOI / ID Number: | 10.1142/S0129065719500163 |
Copyright Information: | Copyright 2019 World Scientific Publishing Company |
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