In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method

10.1016/j.compbiomed.2013.01.015

Saved in:

Bibliographic Details
Main Authors:	Li, B.-K., Cong, Y., Yang, X.-G., Xue, Y., Chen, Y.-Z.
Other Authors:	PHARMACY
Format:	Article
Published:	2014
Subjects:	Machine learning methods (ML methods) Recursive feature elimination (RFE) Rheumatoid arthritis (RA) Spleen tyrosine kinase (Syk) Support vector machine (SVM)
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/106035
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	National University of Singapore

id	sg-nus-scholar.10635-106035
record_format	dspace
spelling	sg-nus-scholar.10635-1060352023-10-29T22:05:41Z In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method Li, B.-K. Cong, Y. Yang, X.-G. Xue, Y. Chen, Y.-Z. PHARMACY Machine learning methods (ML methods) Recursive feature elimination (RFE) Rheumatoid arthritis (RA) Spleen tyrosine kinase (Syk) Support vector machine (SVM) 10.1016/j.compbiomed.2013.01.015 Computers in Biology and Medicine 43 4 395-404 CBMDA 2014-10-29T01:54:04Z 2014-10-29T01:54:04Z 2013-05-01 Article 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 00104825 http://scholarbank.nus.edu.sg/handle/10635/106035 000316532100018 Scopus
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Machine learning methods (ML methods) Recursive feature elimination (RFE) Rheumatoid arthritis (RA) Spleen tyrosine kinase (Syk) Support vector machine (SVM)
spellingShingle	Machine learning methods (ML methods) Recursive feature elimination (RFE) Rheumatoid arthritis (RA) Spleen tyrosine kinase (Syk) Support vector machine (SVM) Li, B.-K. Cong, Y. Yang, X.-G. Xue, Y. Chen, Y.-Z. In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
description	10.1016/j.compbiomed.2013.01.015
author2	PHARMACY
author_facet	PHARMACY Li, B.-K. Cong, Y. Yang, X.-G. Xue, Y. Chen, Y.-Z.
format	Article
author	Li, B.-K. Cong, Y. Yang, X.-G. Xue, Y. Chen, Y.-Z.
author_sort	Li, B.-K.
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
title_short	In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
title_full	In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
title_fullStr	In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
title_full_unstemmed	In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
title_sort	in silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
publishDate	2014
url	http://scholarbank.nus.edu.sg/handle/10635/106035
_version_	1781788160976486400

In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method

Similar Items