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Record W2035820679 · doi:10.1586/epr.10.95

A synopsis of the 3rd annual Cancer Proteomics Conference

2010· article· en· W2035820679 on OpenAlexaboutno aff
Mehdi Mesri, Christopher R. Kinsinger, Emily S. Boja, Tara Hiltke, Amir Rahbar, Robert Rivers, Henry Rodriguez

Bibliographic record

VenueExpert Review of Proteomics · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsProteomicsBiomarker discoveryData scienceProteogenomicsLibrary scienceComputer scienceBiologyGenomics

Abstract

fetched live from OpenAlex

The 3rd annual 'Cancer Proteomics Conference', organized by Select Biosciences (Sudbury, UK), was held in Berlin, Germany, 8-9 June 2010. With the aim of strengthening the links between scientists from Europe, as well as international investigators worldwide, more than 200 delegates attended, representing various countries. The Conference covered many topics in proteomics, including the use of proteomics for cancer therapeutic development, diagnostic applications, biomarker discovery, post-translational modifications and clinical proteomics, as well as new proteomic technologies, which may facilitate future progress. One distinct feature of this meeting was that the Conference was co-located with the 'Advances in Antibody and Peptide Therapeutics' meeting. Delegates had access to both meetings, allowing for enhanced interaction among investigators from the closely linked fields of research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0430.028

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 head0.013
GPT teacher head0.327
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2010
Admission routes1
Has abstractyes

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