Evidence map›Paper›PMID 24895432›Full record

ArticleNucleic acids research2014

CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra.

Felicity Allen, Allison Pon, Michael Wilson, Russ Greiner, David Wishart

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 210 papers.

0numbers the graph read from it
0cells of the map it votes in
210citing papers in PubMed
9.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

210 citing papers in PubMed, 476 citations in OpenAlex.

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  9. Hybrid Deep Learning Model for EI-MS Spectra Prediction.International journal of molecular sciences · 2026
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150 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Felicity AllenDepartment of Computing Science, Athabasca Hall, University of Alberta, Edmonton T6G 2E8, Canada. felicity.allen@ualberta.ca.
Allison PonDepartment of Computing Science, Athabasca Hall, University of Alberta, Edmonton T6G 2E8, Canada.
Michael WilsonDepartment of Computing Science, Athabasca Hall, University of Alberta, Edmonton T6G 2E8, Canada.
Russ GreinerDepartment of Computing Science, Athabasca Hall, University of Alberta, Edmonton T6G 2E8, Canada.
David WishartDepartment of Computing Science, Athabasca Hall, University of Alberta, Edmonton T6G 2E8, Canada.
Athabasca University · CAUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CFM-ID is a web server supporting three tasks associated with the interpretation of tandem mass spectra (MS/MS) for the purpose of automated metabolite identification: annotation of the peaks in a spectrum for a known chemical structure; prediction of spectra for a given chemical structure and putative metabolite identification--a predicted ranking of possible candidate structures for a target spectrum. The algorithms used for these tasks are based on Competitive Fragmentation Modeling (CFM), a recently introduced probabilistic generative model for the MS/MS fragmentation process that uses machine learning techniques to learn its parameters from data. These algorithms have been extensively tested on multiple datasets and have been shown to out-perform existing methods such as MetFrag and FingerId. This web server provides a simple interface for using these algorithms and a graphical display of the resulting annotations, spectra and structures. CFM-ID is made freely available at http://cfmid.wishartlab.com.

Indexed as

SoftwareAlgorithmsArtificial IntelligenceHumansInternetMetabolomicsModels, StatisticalTandem Mass Spectrometry

Identifiers

PMID24895432
PMCPMC4086103
OpenAlexW2101394823

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.