Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors

Publisher： Bentham Science Publishers

E-ISSN： 1875-5402|19|5|400-411

ISSN： 1386-2073

Source： Combinatorial Chemistry & High Throughput Screening, Vol.19, Iss.5, 2016-06, pp. : 400-411

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

B‐factor profile prediction for RNA flexibility using support vector machines

By  

JOURNAL OF COMPUTATIONAL CHEMISTRY, Vol. 39, Iss. 8, 2018-03 ,pp. : 407-411

John Wiley & Sons Inc

Access to resources Recommend Favorite

Support Vector Machine Based Prediction of P. falciparum Proteasome Inhibitors and Development of Focused Library by Molecular Docking

By  

Combinatorial Chemistry & High Throughput Screening, Vol. 14, Iss. 10, 2011-12 ,pp. : 898-907

Bentham Science Publishers

Access to resources Recommend Favorite

Classification of Acetylcholinesterase Inhibitors and Decoys by a Support Vector Machine

By  

Combinatorial Chemistry & High Throughput Screening, Vol. 15, Iss. 6, 2012-07 ,pp. : 492-502

Bentham Science Publishers

Access to resources Recommend Favorite

Natural Products Derived from Traditional Chinese Medicine as Novel Inhibitors of the Epidermal Growth Factor Receptor

By  

Combinatorial Chemistry & High Throughput Screening, Vol. 13, Iss. 10, 2010-12 ,pp. : 849-854

Bentham Science Publishers

Access to resources Recommend Favorite

Classification of Active and Weakly Active ST Inhibitors of HIV-1 Integrase Using a Support Vector Machine

By  

Combinatorial Chemistry & High Throughput Screening, Vol. 15, Iss. 10, 2012-12 ,pp. : 792-805

Bentham Science Publishers

Access to resources Recommend Favorite