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Bioactive molecule prediction using majority voting-based ensemble method

The current rise in the amount of data generated has necessitated the use of machine learning in the drug discovery process to increase productivity. It is therefore important to predict molecular compounds which are biologically active and capable of drug-target interaction. Various machine learnin...

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Main Authors: Petinrin, Olutomilayo Olayemia, Saeed, Faisal
Format: Article
Published: IOS Press 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/84639/
http://dx.doi.org/10.3233/JIFS-169596
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id my.utm.84639
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spelling my.utm.846392020-02-27T03:21:19Z http://eprints.utm.my/id/eprint/84639/ Bioactive molecule prediction using majority voting-based ensemble method Petinrin, Olutomilayo Olayemia Saeed, Faisal QA75 Electronic computers. Computer science The current rise in the amount of data generated has necessitated the use of machine learning in the drug discovery process to increase productivity. It is therefore important to predict molecular compounds which are biologically active and capable of drug-target interaction. Various machine learning methods have been used in predicting bioactive molecular compounds in order to deal with the large volume of data being generated. This study investigates the Majority Voting ensemble method using different combinations of 5 commonly-used machine learning algorithms, including Support Vector Machine, Decision Tree, Naïve Bayes, k-Nearest Neighbor, and Random Forest on three chemical datasets DS1, DS2, and DS3 which consist of structurally heterogeneous and homogeneous molecules and are commonly used in other studies. The results show that Majority Voting has a better performance, based on all the evaluation metrics used, compared to each of the machine learning algorithms as individual classifiers. It also shows the Majority Voting ensemble method as effective in the prediction of both heterogeneous and homogeneous bioactive molecular compounds, using statistical evaluation. IOS Press 2018 Article PeerReviewed Petinrin, Olutomilayo Olayemia and Saeed, Faisal (2018) Bioactive molecule prediction using majority voting-based ensemble method. Journal of Intelligent & Fuzzy Systems, 35 (1). pp. 383-392. ISSN 1064-1246 http://dx.doi.org/10.3233/JIFS-169596
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Petinrin, Olutomilayo Olayemia
Saeed, Faisal
Bioactive molecule prediction using majority voting-based ensemble method
description The current rise in the amount of data generated has necessitated the use of machine learning in the drug discovery process to increase productivity. It is therefore important to predict molecular compounds which are biologically active and capable of drug-target interaction. Various machine learning methods have been used in predicting bioactive molecular compounds in order to deal with the large volume of data being generated. This study investigates the Majority Voting ensemble method using different combinations of 5 commonly-used machine learning algorithms, including Support Vector Machine, Decision Tree, Naïve Bayes, k-Nearest Neighbor, and Random Forest on three chemical datasets DS1, DS2, and DS3 which consist of structurally heterogeneous and homogeneous molecules and are commonly used in other studies. The results show that Majority Voting has a better performance, based on all the evaluation metrics used, compared to each of the machine learning algorithms as individual classifiers. It also shows the Majority Voting ensemble method as effective in the prediction of both heterogeneous and homogeneous bioactive molecular compounds, using statistical evaluation.
format Article
author Petinrin, Olutomilayo Olayemia
Saeed, Faisal
author_facet Petinrin, Olutomilayo Olayemia
Saeed, Faisal
author_sort Petinrin, Olutomilayo Olayemia
title Bioactive molecule prediction using majority voting-based ensemble method
title_short Bioactive molecule prediction using majority voting-based ensemble method
title_full Bioactive molecule prediction using majority voting-based ensemble method
title_fullStr Bioactive molecule prediction using majority voting-based ensemble method
title_full_unstemmed Bioactive molecule prediction using majority voting-based ensemble method
title_sort bioactive molecule prediction using majority voting-based ensemble method
publisher IOS Press
publishDate 2018
url http://eprints.utm.my/id/eprint/84639/
http://dx.doi.org/10.3233/JIFS-169596
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score 13.211869

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