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EMSL-Computing/PeakDecoder

RESOURCE

Publicly Accessible Repository
https://github.com/EMSL-Computing/PeakDecoder
https://doi.org/10.11578/dc.20240614.237

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Abstract

PeakDecoder is a machine learning (ML)-based metabolite identification algorithm for multidimensional mass spectrometry measurements incorporating liquid chromatography (LC) and ion mobility spectrometry (IM) separations, and collecting extensive fragmentation spectra with data-independent acquisition (DIA) methods: LC-IM-DIA-MS. The algorithm learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates.
Developers:
Bilbao Pena, Aivett [1]
  1. Pacific Northwest National Laboratory
Release Date:
2022-09-07
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 2-clause "Simplified" License
Sponsoring Org.:
Code ID:
80693
Site Accession Number:
Battelle IPID 32522-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/EMSL-Computing/PeakDecoder
https://doi.org/10.11578/dc.20240614.237

SAVE / SHARE



PNNL Badge

Citation Formats

Bilbao Pena, Aivett. EMSL-Computing/PeakDecoder. Computer Software. https://github.com/EMSL-Computing/PeakDecoder. USDOE. 07 Sep. 2022. Web. doi:10.11578/dc.20240614.237.
Bilbao Pena, Aivett. (2022, September 07). EMSL-Computing/PeakDecoder. [Computer software]. https://github.com/EMSL-Computing/PeakDecoder. https://doi.org/10.11578/dc.20240614.237.
Bilbao Pena, Aivett. "EMSL-Computing/PeakDecoder." Computer software. September 07, 2022. https://github.com/EMSL-Computing/PeakDecoder. https://doi.org/10.11578/dc.20240614.237.
@misc{ doecode_80693,
title = {EMSL-Computing/PeakDecoder},
author = {Bilbao Pena, Aivett},
abstractNote = {PeakDecoder is a machine learning (ML)-based metabolite identification algorithm for multidimensional mass spectrometry measurements incorporating liquid chromatography (LC) and ion mobility spectrometry (IM) separations, and collecting extensive fragmentation spectra with data-independent acquisition (DIA) methods: LC-IM-DIA-MS. The algorithm learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates.},
doi = {10.11578/dc.20240614.237},
url = {https://doi.org/10.11578/dc.20240614.237},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240614.237}},
year = {2022},
month = {sep}
}