DICP OpenIR
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
KeywordK-nearest Neighbor (K-nn) Kernel Methods String Kernel Structure-activity Relationship (Sar)
Source PublicationCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
2012-05-15
DOI10.1016/j.chemolab.2012.01.008
Volume114Pages:19-23
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
WOS SubjectAutomation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligence ; Instruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS Research AreaAutomation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
WOS KeywordPREDICTION ; SELECTION ; CLASSIFICATION ; AGENTS
AbstractA 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.
Language英语
WOS IDWOS:000304734400003
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/142972
Collection中国科学院大连化学物理研究所
Affiliation1.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
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Cao, Dong-Sheng]'s Articles
[Huang, Jian-Hua]'s Articles
[Yan, Jun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Cao, Dong-Sheng]'s Articles
[Huang, Jian-Hua]'s Articles
[Yan, Jun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cao, Dong-Sheng]'s Articles
[Huang, Jian-Hua]'s Articles
[Yan, Jun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.