spec2vec

spec2vec is a novel similarity measure for comparing mass spectrometry data, which learns peak representations using Word2Vec.

18
mentions
10
contributors

Cite this software

DOI:

10.5281/zenodo.3873168

What spec2vec can do for you

  • Allows to learn abstract mass spectra representations from large mass spectral data sets (unsupervised learning).
  • Computes mass spectra similarities that show a high correlation with actual molecular similarity.

Spec2vec is a novel spectral similarity score inspired by a natural language processing
algorithm -- Word2Vec. Where Word2Vec learns relationships between words in sentences,
spec2vec does so for mass fragments and neutral losses in MS/MS spectra.
The spectral similarity score is based on spectral embeddings learnt
from the fragmental relationships within a large set of spectral data.

Keywords
  • Machine learning
  • Text analysis & natural language processing
Programming language
  • Python 98%
  • Batchfile 1%
  • Makefile 1%
License
  • Apache-2.0
</>Source code

Participating organisations

Netherlands eScience Center
Wageningen University & Research

Mentions

Build a mass spectrometry analysis pipeline in Python using matchms — part II: Spec2Vec

Author(s): Florian Huber
Published in 2021

Build a mass spectrometry analysis pipeline in Python using matchms — part III: molecular…

Author(s): Florian Huber
Published in 2021

Contributors

Contact person

Florian Huber

Florian Huber

Netherlands eScience Center
Mail Florian
AB
Adam Belloum
Netherlands eScience Center
Christiaan Meijer
Christiaan Meijer
Netherlands eScience Center
Cunliang Geng
Cunliang Geng
Netherlands eScience Center
Faruk Diblen
Faruk Diblen
Netherlands eScience Center
Florian Huber
Florian Huber
Netherlands eScience Center
HS
Hanno Spreeuw
Netherlands eScience Center
Jurriaan H. Spaaks
Jurriaan H. Spaaks
Netherlands eScience Center
JvdH
Justin J. J. van der Hooft
Wageningen University & Research
SR
Simon Rogers
University of Glasgow
Stefan Verhoeven
Stefan Verhoeven
Netherlands eScience Center

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13 mentions, 14 contributors