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dc.contributor.authorZeng, Xiangyuan
dc.date.accessioned2021-05-13 18:27:57 (GMT)
dc.date.available2021-05-13 18:27:57 (GMT)
dc.date.issued2021-05-13
dc.date.submitted2021-05-07
dc.identifier.urihttp://hdl.handle.net/10012/16980
dc.description.abstractLiquid chromatography with tandem mass spectrometry (LC-MS/MS) has been widely used in proteomics. Two types of data, MS and MS/MS data, are produced in an LC- MS/MS experiment. The MS data contains signal peaks corresponding to the intact pep- tides in the samples being analyzed. However, research on protein mass spectrometry data has focused more on extracting information from MS/MS data than on MS data. To effectively utilize MS information, we propose a novel software tool, MSTracer, to detect peptide features from MS data. Two machine-learning-combined scoring functions were incorporated in the implementation: one for detecting the peptide features and another for assigning a quality score to each detected peptide feature. The software was compared with several other tools and demonstrated significantly better performance.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.relation.urihttp://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012238en
dc.relation.urihttp://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD006631en
dc.relation.urihttp://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD022287en
dc.subjectbioinformaticsen
dc.subjectmass spectrometryen
dc.subjectpeptide feature detectionen
dc.titleA Machine-Learning-Based Algorithm for Peptide Feature Detection from Protein Mass Spectrometry Dataen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws-etd.embargo.terms0en
uws.contributor.advisorMa, Bin
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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