Name
Abstract | ||
|---|---|---|
The DNA microarray technology is about to bring an explosion of gene expression data that may dwarf even the human sequencing projects. Researchers are motivated to identify genes whose expression levels rise and fall coherently under a set of experimental perturbances, that is, they exhibit fluctuation of a similar shape when conditions change. In this paper, we show that queries based on pattern correlations against large-scale microarray databases can be supported by the weighted-sequence model, an index structure designed for sequence matching. A weighted-sequence is a two-dimensional structure where each element in the sequence is associated with a weight. We transform the DNA microarray data, as well as pattern-based queries, into weighted-sequences, and use subsequence matching algorithms to retrieve from the database all genes that match the query pattern. We demonstrate, using both synthetic and real-world data sets, that our method is effective and efficient. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/CSB.2002.1039348 | CSB |
Keywords | Field | DocType |
pattern-based queries,pattern correlations,query pattern,queries,dna microarray data,pattern similarity searching,arrays,dna,index structure,genetics,2d structure,scientific information systems,expression levels rise,dna microarray technology,pattern matching,sequence matching,database indexing,biology computing,sequences,molecular biophysics,pattern correlation,gene expression data,large-scale microarray databases,very large databases,matching algorithms,fluctuation,real-world data set,weighted-sequence model,query processing,dna microarray,similarity search | Data set,Gene,Computer science,Bioinformatics,Gene chip analysis,Subsequence,Microarray databases,Database index,Pattern matching,DNA microarray | Conference |
Volume | ISSN | ISBN |
1 | 1555-3930 | 0-7695-1653-X |
Citations | PageRank | References |
3 | 0.39 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Wang | 1 | 5539 | 275.36 |
| Chang-Shing Perng | 2 | 478 | 35.92 |
| Wei Fan | 3 | 4205 | 253.58 |
| Philip S. Yu | 4 | 30670 | 3474.16 |