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Record W2766218091 · doi:10.1021/acs.analchem.7b04340

Proteomic and Metaproteomic Approaches to Understand Host–Microbe Interactions

2017· review· en· W2766218091 on OpenAlexafffundabout
Amanda E. Starr, Shelley A. Deeke, Leyuan Li, Xu Zhang, Rachid Daoud, James W. Ryan, Zhibin Ning, Kai Cheng, Linh V. H. Nguyen, Elias Abou-Samra, Mathieu Lavallée‐Adam, Daniel Figeys

Bibliographic record

VenueAnalytical Chemistry · 2017
Typereview
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaGenome CanadaCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsLibrary scienceCitationSocial mediaWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTProteomic and Metaproteomic Approaches to Understand Host–Microbe InteractionsAmanda E. Starr*†, Shelley A. Deeke†, Leyuan Li†, Xu Zhang†, Rachid Daoud†, James Ryan†, Zhibin Ning†, Kai Cheng†, Linh V. H. Nguyen†, Elias Abou-Samra†, Mathieu Lavallée-Adam†, and Daniel Figeys*†‡§View Author Information† Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada‡ Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada§ Molecular Architecture of Life Program, Canadian Institute for Advanced Research, Toronto, Ontario, M5G 1M1, Canada*E-mail: [email protected]*E-mail: [email protected]Cite this: Anal. Chem. 2018, 90, 1, 86–109Publication Date (Web):October 23, 2017Publication History Published online9 November 2017Published inissue 2 January 2018https://doi.org/10.1021/acs.analchem.7b04340Copyright © 2017 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views7700Altmetric-Citations31LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (3 MB) Get e-AlertsSUBJECTS:Bacteria,Biological databases,Peptides and proteins,Protein identification,Proteomics Get e-Alerts

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.275
GPT teacher head0.389
Teacher spread0.115 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations69
Published2017
Admission routes3
Has abstractyes

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