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Record W1952729546 · doi:10.1517/17530059.1.1.147

Highlights of the Third Annual Biomarker World Congress: biomarkers for molecular diagnostics

2007· article· en· W1952729546 on OpenAlexaff
Michael E. Burczynski

Bibliographic record

VenueExpert Opinion on Medical Diagnostics · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsBiomarkerBiomarker discoveryDrug developmentMedicineMolecular biomarkersProfiling (computer programming)Computational biologyBioinformaticsProteomicsDrugOncologyBiologyPharmacologyComputer science

Abstract

fetched live from OpenAlex

With the proliferation of high-content profiling technologies available for analyzing genetic, transcriptomic, proteomic and metabolomic markers in tissues, both novel analytes and increasingly complex signatures are emerging as biomarkers supporting drug development. If prospective studies during the course of clinical development support the clinical utility of a biomarker, consideration may eventually be given to converting such research-use only biomarker assays into medical diagnostics. The present report briefly summarizes a selected set of presentations geared towards the issues and principles involved in the validation of biomarkers for use as molecular diagnostics, as presented at the Third Annual Biomarker World Congress held in Philadelphia, Pennsylvania on 15 - 18 May 2007. More than 400 delegates attended the Third Annual Biomarker World Congress to discuss the role of biomarkers in all phases of drug discovery and development. The main conference consisted of four tracks addressing issues involving: i) biomarkers in early drug development; ii) biomarkers in clinical development; iii) biomarkers for molecular diagnostics; and iv) biomarker assay development. The present review focuses on selected presentations of relevance to the third track. Topics covered by the speakers included biomarker assays that seem to have diagnostic and/or theranostic potential in a variety of therapeutic settings and disease indications. Themes included both the general and specific issues that can be encountered when attempting to convert a biomarker assay into a molecular diagnostic for use in the clinical setting.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
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 integrity0.0010.000
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 head0.025
GPT teacher head0.340
Teacher spread0.315 · 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
GenreEmpirical

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
Published2007
Admission routes1
Has abstractyes

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