DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction
Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoret...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-09-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020006 |
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| _version_ | 1832543033772474368 |
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| author | Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun |
| author_facet | Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun |
| author_sort | Yihui Ren |
| collection | DOAJ |
| description | Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoretical spectra in mass spectrometry. DMSS has undergone rigorous validation through a series of experiments, consistently demonstrating superior performance compared to current methods in forecasting theoretical spectra. The superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein identification. These findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field. |
| format | Article |
| id | doaj-art-a690b177a7674de7b78e3c137ad34645 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a690b177a7674de7b78e3c137ad346452025-02-03T11:53:24ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-017357758910.26599/BDMA.2024.9020006DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry PredictionYihui Ren0Yu Wang1Wenkai Han2Yikang Huang3Xiaoyang Hou4Chunming Zhang5Dongbo Bu6Xin Gao7Shiwei Sun8Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with University of Chinese Academy of Sciences, Beijing 100049, ChinaSyneron Technology, Guangzhou 510000, ChinaComputer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi ArabiaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaKey Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with University of Chinese Academy of Sciences, Beijing 100049, ChinaInsitute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with Western Institute of Computing Technology, Chongqing 400000, ChinaKey Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with University of Chinese Academy of Sciences, Beijing 100049, ChinaComputer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi ArabiaKey Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with University of Chinese Academy of Sciences, Beijing 100049, ChinaAccurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoretical spectra in mass spectrometry. DMSS has undergone rigorous validation through a series of experiments, consistently demonstrating superior performance compared to current methods in forecasting theoretical spectra. The superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein identification. These findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.https://www.sciopen.com/article/10.26599/BDMA.2024.9020006mass spectrometryproteomicsmachine learningdeep learning |
| spellingShingle | Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction Big Data Mining and Analytics mass spectrometry proteomics machine learning deep learning |
| title | DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction |
| title_full | DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction |
| title_fullStr | DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction |
| title_full_unstemmed | DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction |
| title_short | DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction |
| title_sort | dmss an attention based deep learning model for high quality mass spectrometry prediction |
| topic | mass spectrometry proteomics machine learning deep learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020006 |
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