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基于 DeepMRM 的靶向蛋白质组学数据解读。

Targeted proteomics data interpretation with DeepMRM.

机构信息

Bertis, Inc., Seoul 06108, Republic of Korea.

Bertis Bioscience, Inc., San Diego, CA 92121, USA.

出版信息

Cell Rep Methods. 2023 Jul 11;3(7):100521. doi: 10.1016/j.crmeth.2023.100521. eCollection 2023 Jul 24.

Abstract

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.

摘要

靶向蛋白质组学在临床蛋白质组学中得到了广泛应用;然而,研究人员通常需要花费大量时间进行手动数据解释,这阻碍了该方法的可转移性、可重复性和可扩展性。我们介绍了 DeepMRM,这是一个基于深度学习算法的软件包,用于目标蛋白质组学数据分析中的对象检测,旨在最大限度地减少手动干预。DeepMRM 在内部和公共数据集上进行了评估,与社区标准工具 Skyline 相比,具有更高的准确性。为了促进广泛采用,我们为 DeepMRM 整合了一个独立的图形用户界面,并将其算法集成到 Skyline 软件包中作为外部工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b191/10391571/e4f734b9ae35/fx1.jpg

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