tetano
Editor, Senior Moderator
SAR QSAR Environ Res. 2018 Jul 23:1-11. doi: 10.1080/1062936X.2018.1491414. [Epub ahead of print]
[h=1]A binary QSAR model for classifying neuraminidase inhibitors of influenza A viruses (H1N1) using the combined minimum redundancy maximum relevancy criterion with the sparse support vector machine.[/h] Qasim MK[SUP]1[/SUP], Algamal ZY[SUP]2[/SUP], Ali HTM[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Quantitative structure-activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors. Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors. In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine. The experimental results of classifying the neuraminidase inhibitors of influenza A (H1N1) viruses show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors.
[h=4]KEYWORDS:[/h] Sparse support vector machine; descriptor selection; influenza A viruses; lasso; minimum redundancy maximum relevancy
PMID: 30037283 DOI: 10.1080/1062936X.2018.1491414
[h=1]A binary QSAR model for classifying neuraminidase inhibitors of influenza A viruses (H1N1) using the combined minimum redundancy maximum relevancy criterion with the sparse support vector machine.[/h] Qasim MK[SUP]1[/SUP], Algamal ZY[SUP]2[/SUP], Ali HTM[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Quantitative structure-activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors. Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors. In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine. The experimental results of classifying the neuraminidase inhibitors of influenza A (H1N1) viruses show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors.
[h=4]KEYWORDS:[/h] Sparse support vector machine; descriptor selection; influenza A viruses; lasso; minimum redundancy maximum relevancy
PMID: 30037283 DOI: 10.1080/1062936X.2018.1491414