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However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target\u2013decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.<\/jats:p>","DOI":"10.1093\/bib\/bbac214","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T11:07:15Z","timestamp":1652094435000},"source":"Crossref","is-referenced-by-count":10,"title":["DeepSCP: utilizing deep learning to boost single-cell proteome coverage"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0684-2789","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Medicine , Southeast University, Nanjing 210009 , China"},{"name":"Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , 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