Proteomics: From Technology Developments to Biological Applications
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEReviewNEXTProteomics: From Technology Developments to Biological ApplicationsMohamed Abu-Farha, Fred Elisma, Houjiang Zhou, Ruijun Tian, Hu Zhou, Mehmet Selim Asmer, and Daniel Figeys*View Author Information Ottawa Institute of Systems Biology (OISB), University of Ottawa, Ottawa, Ontario, Canada, and Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada* To whom correspondence should be addressed. Daniel Figeys, phone: 613-562-5800 ext 8674. Fax: 613-562-5655. E-mail: [email protected]Cite this: Anal. Chem. 2009, 81, 12, 4585–4599Publication Date (Web):April 16, 2009Publication History Published online16 April 2009Published inissue 15 June 2009https://pubs.acs.org/doi/10.1021/ac900735jhttps://doi.org/10.1021/ac900735jreview-articleACS PublicationsCopyright © 2009 American Chemical SocietyRequest reuse permissionsArticle Views4130Altmetric-Citations41LEARN 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 InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Organic reactions,Peptides and proteins,Post-translational modification,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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".