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题名: Kernel k-nearest neighbor algorithm as a flexible SAR modeling tool
作者: 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
发表日期: 2012-05-15
DOI: 10.1016/j.chemolab.2012.01.008
卷: 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
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.
关键词[WOS]: PREDICTION ;  SELECTION ;  CLASSIFICATION ;  AGENTS
语种: 英语
WOS记录号: WOS:000304734400003
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/142972
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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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

Recommended Citation:
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.
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