.png){kind=icon}
.png){kind=icon}
Return
Targeted proteomics data interpretation with DeepMRM
.png){kind=icon}
OA
AI
DOI：10.1016/j.crmeth.2023.100521.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 detec-tion developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evalu-ated on internal and public datasets, demonstrating superior accuracy compared with the community stan-dard 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.
Keywords:
MS
QUANTIFICATION
ABSOLUTE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
930
Citations:
2.0K