MS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Model
dc.contributor.author | Li, Zhenbo | |
dc.date.accessioned | 2024-02-23T14:51:28Z | |
dc.date.available | 2024-02-23T14:51:28Z | |
dc.date.issued | 2024-02-23 | |
dc.date.submitted | 2024-02-14 | |
dc.description.abstract | To 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.uri | http://hdl.handle.net/10012/20364 | |
dc.language.iso | en | en |
dc.pending | false | |
dc.publisher | University of Waterloo | en |
dc.subject | Transfer Learning | en |
dc.subject | MHC | en |
dc.subject | Mass Spectra Prediction | en |
dc.subject | Mass spectrometry | en |
dc.title | MS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Model | en |
dc.type | Master Thesis | en |
uws-etd.degree | Master of Mathematics | en |
uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
uws-etd.degree.discipline | Computer Science | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.embargo.terms | 0 | en |
uws.comment.hidden | My 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.advisor | Ma, Bin | |
uws.contributor.advisor | Lu, Yang | |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.peerReviewStatus | Unreviewed | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.scholarLevel | Graduate | en |
uws.typeOfResource | Text | en |