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Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool
Cao, Dong-Sheng2; Huang, Jian-Hua2; Yan, Jun2; Zhang, Liang-Xiao3; Hu, Qian-Nan4,5; Xu, Qing-Song1; Liang, Yi-Zeng2
关键词K-nearest Neighbor (K-nn) Kernel Methods String Kernel Structure-activity Relationship (Sar)
刊名CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
2012-05-15
DOI10.1016/j.chemolab.2012.01.008
114页:19-23
收录类别SCI
文章类型Article
WOS标题词Science & Technology ; Technology ; Physical Sciences
类目[WOS]Automation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligence ; Instruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
研究领域[WOS]Automation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
关键词[WOS]PREDICTION ; SELECTION ; CLASSIFICATION ; AGENTS
英文摘要A kernel version of k-nearest neighbor algorithm (k-NN) has been developed to model the complex relationship between molecular descriptors and bioactivities of compounds. Kernel k-NN is to perform the original k-NN algorithm by mapping the training samples in the input space into a high-dimensional feature space. It can be easily constructed by calculating the distance between samples in the feature space, directly deriving from the simple calculation of the kernel used. The developed kernel k-NN is very flexible to deal with complex nonlinear relationship, more importantly; it can also conveniently cope with some non-vectorial data only by the definition of different kernels. The results obtained from several real SAR datasets indicated that the performance of kernel k-NN is comparable to support vector machine methods. It can be regarded as an alternative modeling technique for several chemical problems including the study of structure-activity relationship (SAR). The source codes implementing kernel k-NN in R language are freely available at http://code.google.com/p/kernelmethods/. (C) 2012 Elsevier B.V. All rights reserved.
语种英语
WOS记录号WOS:000304734400003
引用统计
被引频次:6[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://cas-ir.dicp.ac.cn/handle/321008/142972
专题中国科学院大连化学物理研究所
作者单位1.Cent S Univ, Sch Math Sci & Comp Technol, Changsha 410083, Peoples R China
2.Cent S Univ, Res Ctr Modernizat Tradit Chinese Med, Changsha 410083, Peoples R China
3.Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
4.Wuhan Univ, Key Lab Combinatorial Biosynth & Drug Discovery, Minist Educ, Wuhan 430071, Peoples R China
5.Wuhan Univ, Sch Pharmaceut Sci, Wuhan 430071, Peoples R China
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GB/T 7714
Cao, Dong-Sheng,Huang, Jian-Hua,Yan, Jun,et al. Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,2012,114:19-23.
APA Cao, Dong-Sheng.,Huang, Jian-Hua.,Yan, Jun.,Zhang, Liang-Xiao.,Hu, Qian-Nan.,...&Liang, Yi-Zeng.(2012).Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,114,19-23.
MLA Cao, Dong-Sheng,et al."Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 114(2012):19-23.
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