Journal ArticleDOI
A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.
TLDR
The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets.Abstract:
A high-dimensional quantitative structure–activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.read more
Citations
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Artificial Intelligence for Drug Toxicity and Safety.
TL;DR: Recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches are explored.
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Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.
TL;DR: DNN-QSAR demonstrated excellent performance in the discipline of computational chemistry by handling multicollinearity and overfitting problems, and was superior for assessment qualitative responses.
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A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine.
TL;DR: The experimental results from classifying the neuraminidase inhibitors of influenza A viruses (H1N1) demonstrate that the proposed method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance and the number of selected descriptors.
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Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression
TL;DR: An improving grasshopper optimization algorithm is proposed by adapting a new function of the main controlling parameter of GoA to enhance the exploration and exploitation capability of GOA to optimize the hyperparameters of the SVR with embedding the feature selection simultaneously.
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Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression
TL;DR: The experimental results prove that the proposed Bayesian Lasso method for gene selection in binary classification regression is very effective for selecting the most relevant genes with high classification accuracy.
References
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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Jianqing Fan,Runze Li +1 more
TL;DR: In this article, penalized likelihood approaches are proposed to handle variable selection problems, and it is shown that the newly proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well if the correct submodel were known.
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