{"type":"ScholarlyArticle","title":"Improving Tandem Mass Spectra Analysis with Hierarchical Learning","authors":[{"name":"Zhengcong Fei","slug":"zhengcong-fei","given":"Zhengcong","family":"Fei","affiliation":"Key Lab of Intell. Info. Process. , Inst. of Comput. Tech. , CAS , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"}],"abstract":"Tandem mass spectrometry is the most widely used technology to identify proteins in a complex biological sample, which produces a large number of spectra representative of protein subsequences named peptide. In this paper, we propose a hierarchical multi-stage framework, referred as DeepTag, to identify the peptide sequence for each given spectrum. Compared with the traditional one-stage generation, our sequencing model starts the inference with a selected high-confidence guiding tag and provides the complete sequence based on this guiding tag. Besides, we introduce a cross-modality refining module to asist the decoder focus on effective peaks and fine-tune with a reinforcement learning technique. Experiments on different public datasets demonstrate that our method achieves a new state-of-the-art performance in peptide identification task, leading to a marked improvement in terms of both precision and recall.","doi":"10.24963/ijcai.2020/599","url":"https://ijcai.org/proceedings/2020/599","pdf":"https://ijcai.org/proceedings/2020/0599.pdf","markdown":"https://ijcai.org/proceedings/2020/599.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/599","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":599,"pages":{"first":4345,"last":4351},"keywords":["Natural Language Processing: NLP Applications and Tools","Computer Vision: Biomedical Image Understanding"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24103/file_24103.m3u8","length_sec":300,"provider":"hls"}],"references":[{"position":1,"raw":"[Anderson et al., 2018] Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 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