典型文献
K-means Find Density Peaks in Molecular Conformation Clustering
文献摘要：
Performing cluster analysis on molecular conformation is an important way to find the representative confor-mation in the molecular dy-namics trajectories.Usu-ally,it is a critical step for interpreting complex con-formational changes or in-teraction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing effi-ciency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n2)to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.
文献关键词：
中图分类号：
作者姓名：
Guiyan Wang;Ting Fu;Hong Ren;Peijun Xu;Qiuhan Guo;Xiaohong Mou;Yan Li;Guohui Li
作者机构：
School of Information Engineering,Dalian Ocean University,Dalian 116029,China;Pharmacy Department of Affiliated Zhongshan Hospital of Dalian University,Dalian 116001,China;State Key Laboratory of Molecular Reaction Dynamics,Dalian Institute of Chemical Physics,Dalian 116023,China;Department of Ophthalmology Aerospace Center Hospital,Beijing 10049,China;Liaoning Normal University,Dalian 116023,China
文献出处：
引用格式：
[1]Guiyan Wang;Ting Fu;Hong Ren;Peijun Xu;Qiuhan Guo;Xiaohong Mou;Yan Li;Guohui Li-.K-means Find Density Peaks in Molecular Conformation Clustering)[J].化学物理学报（英文版）,2022(02):353-368
A类：
confor,Usu,formational,KFDP
B类：
means,Find,Density,Peaks,Molecular,Conformation,Clustering,Performing,analysis,molecular,conformation,important,way,find,representative,dy,namics,trajectories,critical,step,interpreting,changes,teraction,mechanisms,one,density,clustering,algorithms,peaks,accurate,reasonable,candidate,However,facing,rapidly,increasing,simulation,length,due,increase,computing,power,low,limits,potential,Here,marginal,extension,named,solve,mass,source,consuming,problem,In,points,are,initially,clustered,by,high,efficiency,such,centers,typical,which,represents,size,Then,weighted,again,then,refined,core,boundary,redefined,halo,this,has,comparable,accuracy,but,computational,complexity,reduced,from,n2,We,apply,test,method,trajectory,data,multiple,small,proteins,terms,torsion,angle,secondary,structure,contact,map,comparing,results,spatial,applications,noise,show,validation,proposed
AB值：
0.536735
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
