Bioactive molecule prediction using extreme gradient boosting
Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), wh...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/72233/1/FaisalSaeed2016_BioactiveMoleculePredictionUsingExtreme.pdf http://eprints.utm.my/id/eprint/72233/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980378875&doi=10.3390%2fmolecules21080983&partnerID=40&md5=dc9ff9162ad2918c4e08660854dc0a15 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets. |
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