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Ensemble Based Quantification for 18 O Labeled LC-MS
Li,Tianjun; Chen,Long; Zhao,Yin Ping
2018-12-10
Source Publication2018 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2018
Pages194-199
AbstractWith the development of machine learning, researchers can get more accurate results for bioinformatics by using complex machine learning methods. However, these complex machine learning methods are always time consuming and computationally expensive. On the other hand, the methods like boosting in classification can be considered for those traditional 'weak' methods to get better performance. By combining simple regression approaches, an ensemble based method is proposed in this paper for O labeled LC-MS. Experimental results show that this proposed method is capable for better quantification results.
DOI10.1109/ICCSS.2018.8572393
URLView the original
Language英語English
Scopus ID2-s2.0-85060285714
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Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorChen,Long
AffiliationDepartment of Computer and Information Science,University of Macau,Taipa,Macao
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li,Tianjun,Chen,Long,Zhao,Yin Ping. Ensemble Based Quantification for 18 O Labeled LC-MS[C],2018:194-199.
APA Li,Tianjun,Chen,Long,&Zhao,Yin Ping.(2018).Ensemble Based Quantification for 18 O Labeled LC-MS.2018 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2018,194-199.
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