Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities.

ABSTRACT: Machine learning has been an integral part of interpreting data from mass spectrometry (MS)-based proteomics for a long time. Relatively recently, a machine-learning structure appeared successful in other areas of bioinformatics, Transformers. Furthermore, the implementation of Transformers within bioinformatics has become relatively convenient due to transfer learning, i.e., adapting a network trained for other tasks to new functionality. Transfer learning makes these relatively large networks more accessible as it generally requires less data, and the training time improves substantially. We implemented a Transformer based on the pretrained model TAPE to predict MS2 intensities. TAPE is a general model trained to predict missing residues from protein sequences. Despite being trained for

SUBMITTER: Ekvall M 

PROVIDER: S-EPMC9087333 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature