QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics

In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address...

Full description

Saved in:

Bibliographic Details
Main Author:	Zhengyi Zhang (518028) (author)
Other Authors:	Huan Yang (36076) (author), Yanyi Wang (146955) (author), Lei Zhang (38117) (author), Shu-Hai Lin (8275176) (author)
Published:	2025
Subjects:	Biochemistry Biotechnology Space Science Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified using nearly 20 quantifying intricate objects object detection designed liquid chromatography coupled including mzmine 3 https :// github global computation capability feature extraction capabilities even without retraining convolutional neural networks breast cancer patients average precision value 000 annotated regions higher accurate quantification incorrectly picked peaks metabolomic analysis based metabolomic analysis picked peaks quantification tool based metabolomics false peaks test set potential biomarkers peak alignment massive job mass spectrometry larger number generally surpass existing methods distinguishing true decoder layers data training clinical cohort also demonstrated also carried algorithm combines achieving 96
Tags:	Add Tag No Tags, Be the first to tag this record!

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics

Similar Items