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Descriptor Selection Improvements for Quantitative Structure-Activity Relationships.

Authors
  • Xia, Liang-Yong1
  • Wang, Qing-Yong2
  • Cao, Zehong3
  • Liang, Yong4
  • 1 Faculty of Information Technology, Macau University of Science and Technology, Macau, P. R. China. , (China)
  • 2 Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, P. R. China. , (China)
  • 3 Discipline of ICT, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, TAS, Australia. , (Australia)
  • 4 University of Science and Technology, Macau, P. R. China. , (China)
Type
Published Article
Journal
International journal of neural systems
Publication Date
Nov 01, 2019
Volume
29
Issue
9
Pages
1950016–1950016
Identifiers
DOI: 10.1142/S0129065719500163
PMID: 31390912
Source
Medline
Keywords
Language
English
License
Unknown

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 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.

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