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• W3199268518 abstract "A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional 'ion mobility' dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well." @default.
• W3199268518 created "2021-09-27" @default.
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• W3199268518 date "2021-09-14" @default.
• W3199268518 modified "2023-10-16" @default.
• W3199268518 title "Deep neural network for detecting arbitrary precision peptide features through attention based segmentation" @default.
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• W3199268518 doi "https://doi.org/10.1038/s41598-021-97669-7" @default.
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