To refer to this page use:
http://arks.princeton.edu/ark:/88435/pr1w96k
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Berger, B | - |
dc.contributor.author | Peng, J | - |
dc.contributor.author | Singh, Mona | - |
dc.date.accessioned | 2018-07-20T15:06:35Z | - |
dc.date.available | 2018-07-20T15:06:35Z | - |
dc.date.issued | 2013-04-18 | en_US |
dc.identifier.citation | Berger, B, Peng, J, Singh, M. (2013). Computational solutions for omics data. Nature Reviews Genetics, 14 (333 - 346. doi:10.1038/nrg3433 | en_US |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr1w96k | - |
dc.description.abstract | High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets. | en_US |
dc.format.extent | 333 - 346 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | Nature Reviews Genetics | en_US |
dc.rights | Author's manuscript | en_US |
dc.title | Computational solutions for omics data | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | doi:10.1038/nrg3433 | - |
dc.date.eissued | 2013-04-18 | en_US |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/journal-article | en_US |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Computational solutions for omics data.pdf | 1.59 MB | Adobe PDF | View/Download |
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.