MS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Model

dc.contributor.authorLi, Zhenbo
dc.date.accessioned2024-02-23T14:51:28Z
dc.date.available2024-02-23T14:51:28Z
dc.date.issued2024-02-23
dc.date.submitted2024-02-14
dc.description.abstractTo improve the quality of spectral library search, several MS/MS spectrum predictors have been developed in the last decades. After success in various fields, deep learning techniques are adopted by MS/MS spectrum predictors to increase the accuracy of predicted spectra. However, the quality and quantity of the training set are both required to train a deep learning model. Due to the less representation of MHC-associated peptides in most spectral libraries, current MS/MS spectrum predictors provide less accurate predicted spectra for MHC-associated peptides than their performance for other peptides. In this thesis, we built several MHC-associated peptide spectral libraries for training and evaluation purposes. We selected PredFull as our base model and performed transfer learning with these MHC-associated peptide libraries, which are much smaller than com- mon tryptic spectral libraries. The result showed that the fine-tuned model outperformed the original model significantly when predicting MHC-associated peptides.en
dc.identifier.urihttp://hdl.handle.net/10012/20364
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectTransfer Learningen
dc.subjectMHCen
dc.subjectMass Spectra Predictionen
dc.subjectMass spectrometryen
dc.titleMS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Modelen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.embargo.terms0en
uws.comment.hiddenMy ORCID is https://orcid.org/my-orcid?orcid=0000-0002-0647-603X I'm sorry for my format issues in my thesis. I appreciate your help to point out them.en
uws.contributor.advisorMa, Bin
uws.contributor.advisorLu, Yang
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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