Name
Abstract | ||
|---|---|---|
The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple datasets simultaneously, rendering it ideal for integrative analysis of expression, phylogenetic and functional genomic data.http://noble.gs.washington.edu/proj/hmmseg |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1093/bioinformatics/btm096 | Bioinformatics |
Keywords | Field | DocType |
high-volume genomic data,large datasets,multiple datasets,multiple scale,functional genomic data,hidden markov model,integrative analysis,unsupervised segmentation,hmmseg contact,command-line utility,continuous genomic data,functional genomics | Data mining,Phylogenetic tree,Pattern recognition,Computer science,Segmentation,Smoothing,Artificial intelligence,Bioinformatics,Rendering (computer graphics),Hidden Markov model,Proj construction,Wavelet | Journal |
Volume | Issue | ISSN |
23 | 11 | 1367-4811 |
Citations | PageRank | References |
16 | 1.31 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan Day | 1 | 27 | 2.25 |
| Andrew Hemmaplardh | 2 | 16 | 1.31 |
| Robert E Thurman | 3 | 46 | 4.46 |
| John A Stamatoyannopoulos | 4 | 94 | 8.35 |
| William S. Noble | 5 | 29 | 4.00 |