Causal interactions from proteomic profiles: Molecular data meet pathway knowledge

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buir.contributor.authorDoğrusöz, Uğur
buir.contributor.orcidDoğrusöz, Uğur|0000-0002-7153-0784
dc.citation.epage100257-12en_US
dc.citation.issueNumber6en_US
dc.citation.spage100257-1en_US
dc.citation.volumeNumber2en_US
dc.contributor.authorBabur, Ö.
dc.contributor.authorLuna, A.
dc.contributor.authorKorkut, A.
dc.contributor.authorDurupınar, F.
dc.contributor.authorSiper, M. C.
dc.contributor.authorDoğrusöz, Uğur
dc.contributor.authorJacome, A. S. V.
dc.contributor.authorPeckner, R.
dc.contributor.authorChristiansen, K. E.
dc.contributor.authorJaffe, J.D
dc.contributor.authorSpellman, P.T.
dc.contributor.authorAslan, J. E.
dc.contributor.authorSander, C.
dc.contributor.authorDemir, E.
dc.date.accessioned2022-02-17T11:57:27Z
dc.date.available2022-02-17T11:57:27Z
dc.date.issued2021-06
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractWe present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.en_US
dc.description.provenanceSubmitted by Samet Emre (samet.emre@bilkent.edu.tr) on 2022-02-17T11:57:27Z No. of bitstreams: 1 Causal_interactions_from_proteomic_profiles_Molecular_data_meet_pathway_knowledge.pdf: 1809127 bytes, checksum: bb040d0fdb894848bde1abe5381209c3 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-02-17T11:57:27Z (GMT). No. of bitstreams: 1 Causal_interactions_from_proteomic_profiles_Molecular_data_meet_pathway_knowledge.pdf: 1809127 bytes, checksum: bb040d0fdb894848bde1abe5381209c3 (MD5) Previous issue date: 2021-06en
dc.embargo.release2022-06-30
dc.identifier.doi10.1016/j.patter.2021.100257en_US
dc.identifier.issn2666-3899
dc.identifier.urihttp://hdl.handle.net/11693/77469
dc.language.isoEnglishen_US
dc.publisherCell Pressen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.patter.2021.100257en_US
dc.source.titlePatternsen_US
dc.subjectProteomicsen_US
dc.subjectCausal pathway analysisen_US
dc.subjectCanceren_US
dc.titleCausal interactions from proteomic profiles: Molecular data meet pathway knowledgeen_US
dc.title.alternativeMolecular data meet pathway knowledgeen_US
dc.typeArticleen_US

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