Targeted proteomics data interpretation with DeepMRM
© 2023 The Authors..
Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
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| Enthalten in: |
Cell reports methods - 3(2023), 7 vom: 24. Juli, Seite 100521 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Park, Jungkap [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 04.08.2023 Date Revised 17.02.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1016/j.crmeth.2023.100521 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360333850 |
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| 520 | |a © 2023 The Authors. | ||
| 520 | |a Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Skyline | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a multiple reaction monitoring | |
| 650 | 4 | |a object detection | |
| 650 | 4 | |a peak detection | |
| 650 | 4 | |a quality control | |
| 650 | 4 | |a quantification | |
| 650 | 4 | |a selected reaction monitoring | |
| 650 | 4 | |a targeted proteomics | |
| 700 | 1 | |a Wilkins, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Avtonomov, Dmitry |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Jiwon |e verfasserin |4 aut | |
| 700 | 1 | |a Back, Seunghoon |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Hokeun |e verfasserin |4 aut | |
| 700 | 1 | |a Shulman, Nicholas |e verfasserin |4 aut | |
| 700 | 1 | |a MacLean, Brendan X |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Sang-Won |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Sangtae |e verfasserin |4 aut | |
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