Bioinformatic analysis of secretory protein candidates

A tétel rövid nézete
dc.contributor.advisorTakács, László
dc.contributor.advisordeptDebreceni Egyetem::Általános Orvostudományi Karhu_HU
dc.contributor.advisordeptHumángenetikai Tanszékhu_HU
dc.contributor.authorNguyen, Thi Huong
dc.contributor.departmentDE--Általános Orvostudományi Karhu_HU
dc.contributor.opponentTzerpos, Petros
dc.contributor.opponentdeptDebreceni Egyetem::Általános Orvostudományi Karhu_HU
dc.contributor.opponentdeptBiokémiai és Molekuláris Biológiai Intézethu_HU
dc.date.accessioned2019-06-20T14:31:21Z
dc.date.available2019-06-20T14:31:21Z
dc.date.created2019-06-12
dc.description.abstractWe used two data sets, first, 200 proteins for which secretion has been described by biological and physicochemical detection outside of the cells and or in bodily fluids, second, we selected 200 intracellular proteins, which have not been detected outside of the cells. We then performed our scoring and analyzed the score distribution histogram in both populations. By applying various thresholds, we were able derive better accuracy than any individual prediction algorithm. The results are encouraging for further studies where we plan to deploy machine learning algorithms for the application of specific weights for individual outcome scores for further optimization of the method. Due to the significance of secreted proteins as potential disease specific biomarkers and therapeutic targets, application of our technology for the entire genome may have an important impact.hu_HU
dc.description.correctorSZG
dc.description.coursemolekuláris biológiahu_HU
dc.description.courselangangolhu_HU
dc.description.coursespecBiokémia-genomikahu_HU
dc.description.degreeMSc/MAhu_HU
dc.format.extent37hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/269906
dc.language.isoenhu_HU
dc.subjectsecreted proteinshu_HU
dc.subjectprediction
dc.subject.dspaceDEENK Témalista::Biológiai tudományokhu_HU
dc.titleBioinformatic analysis of secretory protein candidateshu_HU
Fájlok
Gyűjtemények
Theses (Faculty of Medicine)