MétaCan
Menu
Back to cohort
Record W2032376737 · doi:10.1038/clpt.2008.18

Mass Spectrometry–Based Proteomics: A Useful Tool for Biomarker Discovery?

2008· article· en· W2032376737 on OpenAlexafffund
AO Gramolini, S M Peterman, Thomas Kislinger

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2008
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiomarker discoveryFalse positive paradoxBiomarkerProteomicsComputational biologyMass spectrometryFalse positives and false negativesDrug discoveryComputer scienceBioinformaticsChemistryBiologyChromatographyArtificial intelligence

Abstract

fetched live from OpenAlex

A biomarker is defined as a biological substance (i.e., protein, metabolite, specific post-translational modification) that can be used to detect a disease, measure its progression or the effects of a treatment. Importantly, a biomarker should be readily accessible (i.e., present within body fluids); it must also provide sufficient sensitivity and specificity to accurately distinguish between true positives, false positives, and false negatives. Even more importantly, detection of the biomarker should provide clinical benefits to the patient (i.e., improved survival and/or quality of life). Due to recent technical advances in biomolecular mass spectrometry, a great deal of effort has gone into the discovery of biomarkers at an international level. In this commentary we set forth our views on how mass spectrometry (MS) could be applied to the discovery of elusive biomarkers (Figure 1).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.415
Teacher spread0.310 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations30
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueClinical Pharmacology & TherapeuticsSame topicAdvanced Proteomics Techniques and ApplicationsFrench-language works237,207