The impact of feature selection methods on machine learning-based docking prediction of Indonesian medicinal plant compounds and HIV-1 protease

Rahman Pujianto, Yohanes Gultom, Ari Wibisono, Arry Yanuar, Heru Suhartanto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work evaluates usage feature selection methods to reduce the number of features required to predict docking results between Indonesian medicinal plant compounds and HIV protease. Two feature selection methods, Recursive Feature Elimination (RFE) and Wrapper Method (WM), are trained with a dataset of 7,330 samples and 667 features from PubChem Bioassay and DUD-E decoys. To evaluate the selected features, a dataset of 368 Indonesian herbal chemical compounds labeled by manually docking to PDB HIV-1 protease is used to benchmark the performance of linear SVM classifier using different sets of features. Our experiments show that a set of 471 features selected by RFE and 249 by WM achieve a reduction of classification time by 4.0 and 8.2 seconds respectively. Although the accuracy and sensitivity are also increased by 8% and 16%, no meaningful improvement observed for precision and specificity.

Original languageEnglish
Title of host publication2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages181-186
Number of pages6
ISBN (Electronic)9781728152929
DOIs
Publication statusPublished - Oct 2019
Event11th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019 - Bali, Indonesia
Duration: 12 Oct 201913 Oct 2019

Publication series

Name2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019

Conference

Conference11th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
CountryIndonesia
CityBali
Period12/10/1913/10/19

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