Analyzing causal relationships in proteomic profiles using CausalPath
buir.contributor.author | Doğrusöz, Uğur | |
buir.contributor.orcid | Doğrusöz, Uğur|0000-0002-7153-0784 | |
dc.citation.epage | 12 | en_US |
dc.citation.issueNumber | 4 | en_US |
dc.citation.spage | 1 | en_US |
dc.citation.volumeNumber | 2 | en_US |
dc.contributor.author | Luna, A. | |
dc.contributor.author | Siper, M. C. | |
dc.contributor.author | Korkut, A. | |
dc.contributor.author | Durupinar, F. | |
dc.contributor.author | Aslan, J. E. | |
dc.contributor.author | Sander, C. | |
dc.contributor.author | Demir, E. | |
dc.contributor.author | Babur, O. | |
dc.contributor.author | Doğrusöz, Uğur | |
dc.date.accessioned | 2021-12-28T09:26:55Z | |
dc.date.available | 2021-12-28T09:26:55Z | |
dc.date.issued | 2021-12-17 | |
dc.department | Department of Computer Engineering | en_US |
dc.description.abstract | CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. | en_US |
dc.description.provenance | Submitted by Esma Babayiğit (esma.babayigit@bilkent.edu.tr) on 2021-12-28T09:26:55Z No. of bitstreams: 1 Analyzing_causal_relationships_in_proteomicprofiles_using_CausalPath.pdf: 2733838 bytes, checksum: 96d91acd4610c63a29412b8e912360ef (MD5) | en |
dc.description.provenance | Made available in DSpace on 2021-12-28T09:26:55Z (GMT). No. of bitstreams: 1 Analyzing_causal_relationships_in_proteomicprofiles_using_CausalPath.pdf: 2733838 bytes, checksum: 96d91acd4610c63a29412b8e912360ef (MD5) Previous issue date: 2021-12-17 | en |
dc.identifier.doi | 10.1016/j.xpro.2021.100955 | en_US |
dc.identifier.issn | 2666-1667 | |
dc.identifier.uri | http://hdl.handle.net/11693/76731 | |
dc.language.iso | English | en_US |
dc.publisher | Cell Press | en_US |
dc.relation.isversionof | https://dx.doi.org/10.1016/j.xpro.2021.100955 | en_US |
dc.source.title | STAR Protocols | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Proteomics | en_US |
dc.subject | Systems biology | en_US |
dc.title | Analyzing causal relationships in proteomic profiles using CausalPath | en_US |
dc.type | Article | en_US |
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