A Novel Method of Nonlinear Rapid Feature Selection for High Dimensional Data and Its Application in Peptide QSAR Modeling Based on Support Vector Machine-Reference-Cited by-同舟云学术

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A Novel Method of Nonlinear Rapid Feature Selection for High Dimensional Data and Its Application in Peptide QSAR Modeling Based on Support Vector Machine

Published:2011 Issue:07 Volume:27 Page:1654-1660
ISSN:1000-6818
Container-title:Acta Physico-Chimica Sinica
language:en
Short-container-title:

Author:

Zhi-Jun DAI, ,Wei ZHOU,Zhe-Ming YUAN,

Publisher

Acta Physico-Chimica Sinica & University Chemistry Editorial Office, Peking University

Subject

Physical and Theoretical Chemistry

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving depression prediction using a novel feature selection algorithm coupled with context-aware analysis;Journal of Affective Disorders;2021-12

2. High-accuracy QSAR models of narcosis toxicities of phenols based on various data partition, descriptor selection and modelling methods;RSC Advances;2016

3. Nonlinear QSAR models with high-dimensional descriptor selection and SVR improve toxicity prediction and evaluation of phenols on Photobacterium phosphoreum;Chemometrics and Intelligent Laboratory Systems;2015-07

4. Predicting the Protein Folding Rate Based on Sequence Feature Screening and Support Vector Regression;Acta Physico-Chimica Sinica;2014

5. Multicomplex-Based Pharmacophore and QSAR of Aryl-Sulfamides as Pyruvate Kinase M2 Activators;Acta Physico-Chimica Sinica;2013

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