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:	Refereed Article
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:	5
Deposited By:	Information and Communication Technology
Deposited On:	2019-05-23
Last Modified:	2020-08-17
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