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dc.contributor.authorKedaigle, Amanda Joy
dc.contributor.authorFraenkel, Ernest
dc.date.accessioned2021-11-23T16:03:21Z
dc.date.available2021-10-27T19:51:49Z
dc.date.available2021-11-23T16:03:21Z
dc.date.issued2018-01-01
dc.identifier.issn1064-3745
dc.identifier.urihttps://hdl.handle.net/1721.1/133259.2
dc.description.abstract© 2018, Springer Science+Business Media LLC. With the extraordinary rise in available biological data, biologists and clinicians need unbiased tools for data integration in order to reach accurate, succinct conclusions. Network biology provides one such method for high-throughput data integration, but comes with its own set of algorithmic problems and needed expertise. We provide a step-by-step guide for using Omics Integrator, a software package designed for the integration of transcriptomic, epigenomic, and proteomic data. Omics Integrator can be found at http://fraenkel.mit.edu/omicsintegrator.en_US
dc.language.isoen
dc.publisherSpringer New Yorken_US
dc.relation.isversionof10.1007/978-1-4939-7493-1_2en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleDiscovering altered regulation and signaling through network-based integration of transcriptomic, epigenomic, and proteomic tumor dataen_US
dc.typeArticleen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.relation.journalMethods Mol Biolen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-09-02T18:27:09Z
dspace.orderedauthorsKedaigle, AJ; Fraenkel, Een_US
dspace.date.submission2021-09-02T18:27:12Z
mit.journal.volume1711en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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