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Wei Wang

Researcher at University of California, Los Angeles

Publications -  494
Citations -  29036

Wei Wang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 62, co-authored 458 publications receiving 25588 citations. Previous affiliations of Wei Wang include Fudan University & Association for Computing Machinery.

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Journal ArticleDOI

A haplotype map of the human genome

John W. Belmont, +232 more
TL;DR: A public database of common variation in the human genome: more than one million single nucleotide polymorphisms for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted.
Journal ArticleDOI

A second generation human haplotype map of over 3.1 million SNPs

Kelly A. Frazer, +237 more
- 18 Oct 2007 - 
TL;DR: The Phase II HapMap is described, which characterizes over 3.1 million human single nucleotide polymorphisms genotyped in 270 individuals from four geographically diverse populations and includes 25–35% of common SNP variation in the populations surveyed, and increased differentiation at non-synonymous, compared to synonymous, SNPs is demonstrated.
Proceedings Article

STING: A Statistical Information Grid Approach to Spatial Data Mining

TL;DR: The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered without recourse to the individual objects.
Proceedings ArticleDOI

Efficient mining of frequent subgraphs in the presence of isomorphism

TL;DR: This work proposes a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework it has developed to reduce the number of redundant candidates proposed.
Proceedings ArticleDOI

Clustering by pattern similarity in large data sets

TL;DR: This paper introduces an effective algorithm to detect clusters of genes that are essential in revealing significant connections in gene regulatory networks, and performs tests on several real and synthetic data sets to show its effectiveness.