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Download PDFOpen PDF in browserDeep Active Learning for De Novo Peptide Sequencing from Data-Independent-Acquisition Mass SpectrometryEasyChair Preprint 86307 pages•Date: August 10, 2022AbstractDe novo peptide sequencing from mass spectrom- etry data has been proved as one of the promising methods for the accurate identification of novel peptides. Recently, deep learning has been ap- plied to de novo peptide sequencing using mass spectrometry data. Although numerous mass spec- trometery dataset is publicly available, annotat- ing a large amount of data is too expensive and time-consuming. Therefore, we need a solution for acquiring ms/ms spectra with the high quality rather than a large number of them. By integrat- ing active learning with deep learning, we can reduce the cost of annotation. In this work, we mainly focused on one of the state-of-the-art mod- els, DeepNovo-DIA, which uses convolutional neural networks to MS/MS extract features and long short-term memory to learn the language models of peptides. Instead of selecting spectra randomly to train the DeepNovo-DIA model, we utilized an active learning algorithm to acquire the most informative spectra. We used random selection as the baseline and compared it with two other acquisition strategies. The experiments showed that by integrating active learning with de novo sequencing, we achieve better performance compared to DeepNovo-DIA model for small an- notated spectra. Keyphrases: Decoder/ Encoder, active learning, data-independent acquisition (DIA), de novo peptide sequencing Download PDFOpen PDF in browser |
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