Pathway activity inference using microarray data

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buir.contributor.authorBabur, Özgün
buir.contributor.authorDemir, Emek
buir.contributor.authorAyaz, Aslı
buir.contributor.authorDoğrusöz, Uğur
buir.contributor.authorSakarya, Onur
dc.citation.epage10en_US
dc.citation.spage1en_US
dc.contributor.authorBabur, Özgünen_US
dc.contributor.authorDemir, Emeken_US
dc.contributor.authorAyaz, Aslıen_US
dc.contributor.authorDoğrusöz, Uğuren_US
dc.contributor.authorSakarya, Onuren_US
dc.date.accessioned2020-04-14T07:05:20Z
dc.date.available2020-04-14T07:05:20Z
dc.date.issued2004
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentBilkent Center for Bioinformatics (BCBI)en_US
dc.description.abstractMotivation: Microarray technology provides cell-scale expression data; however, analyzing this data is notoriously difficult. It is becoming clear that system-oriented methods are needed in order to best interpret this data. Combining microarray expression data with previously built pathway models may provide useful insight about the cellular machinery and reveal mechanisms that govern diseases. Given a qualitative state - transition model of the cellular network and an expression profile of RNA molecules, we would like to infer possible differential activity of the other molecules such as proteins on this network. Results: In this paper an efficient algorithm using a new approach is proposed to attack this problem. Using the regulation relations on the network, we determine possible scenarios that might lead to the expression profile, and qualitatively infer the activity differences of the molecules between test and control samples. Availability: This new analysis method has been implemented as part of a microarray data analysis component within PATIKA (Pathway Analysis Tool for Integration and Knowledge Acquisition), which is a software environment for pathway storage, integration and analysis. Facilities for easy analysis and visualization of the results is also provided. Contact: http://www.patika.org.en_US
dc.description.provenanceSubmitted by Evrim Ergin (eergin@bilkent.edu.tr) on 2020-04-14T07:05:20Z No. of bitstreams: 1 Pathway_activity_inference_using_microarray_data.pdf: 303465 bytes, checksum: 1b28cec5825805836663dde238fbb2e6 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-04-14T07:05:20Z (GMT). No. of bitstreams: 1 Pathway_activity_inference_using_microarray_data.pdf: 303465 bytes, checksum: 1b28cec5825805836663dde238fbb2e6 (MD5) Previous issue date: 2004en
dc.identifier.urihttp://hdl.handle.net/11693/53614
dc.language.isoEnglishen_US
dc.publisherBilkent Center for Bioinformatics (BCBI)en_US
dc.subjectRegulatory pathways and networksen_US
dc.subjectGene expression analysisen_US
dc.subjectMicroarray data analysisen_US
dc.subjectSystems biologyen_US
dc.subjectPathway activity inferenceen_US
dc.titlePathway activity inference using microarray dataen_US
dc.typeReporten_US

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