Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compbiomed.2013.01.015
Title: In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
Authors: Li, B.-K.
Cong, Y.
Yang, X.-G.
Xue, Y.
Chen, Y.-Z. 
Keywords: Machine learning methods (ML methods)
Recursive feature elimination (RFE)
Rheumatoid arthritis (RA)
Spleen tyrosine kinase (Syk)
Support vector machine (SVM)
Issue Date: 1-May-2013
Citation: Li, B.-K., Cong, Y., Yang, X.-G., Xue, Y., Chen, Y.-Z. (2013-05-01). In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method. Computers in Biology and Medicine 43 (4) : 395-404. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compbiomed.2013.01.015
Abstract: We tested four machine learning methods, support vector machine (SVM), k-nearest neighbor, back-propagation neural network and C4.5 decision tree for their capability in predicting spleen tyrosine kinase (Syk) inhibitors by using 2592 compounds which are more diverse than those in other studies. The recursive feature elimination method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing Syk inhibitors and non-inhibitors. Among four machine learning models, SVM produces the best performance at 99.18% for inhibitors and 98.82% for non-inhibitors, respectively, indicating that the SVM is potentially useful for facilitating the discovery of Syk inhibitors. © 2013 Elsevier Ltd.
Source Title: Computers in Biology and Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/106035
ISSN: 00104825
DOI: 10.1016/j.compbiomed.2013.01.015
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