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Gene discovery in craniofacial development and disease – cashing in your chips

2007· review· en· W2072081219 on OpenAlexaff
GR Handrigan, Marcela Buchtová, Joy M. Richman

Bibliographic record

VenueClinical Genetics · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCraniofacialDiseaseGeneGeneticsBiologyMedicineComputational biologyInternal medicine

Abstract

fetched live from OpenAlex

An unbiased, polygenic approach is needed to unravel the complex molecular bases of craniofacial development and disease. DNA microarrays, the current paradigm of genome-wide analysis, permit the simultaneous study of many thousands of genes, the ready identification of candidate molecules and pathways, and the compilation of gene expression profiles for whole systems--pathologic and embryonic alike. We survey the existing literature applying microarrays to craniofacial biology and highlight the value of animal models, particularly mice and chickens, to understanding molecular regulation in the craniofacial complex. We also emphasize the importance of functional studies and high-throughput assays to extracting useful data from microarray output. It is our goal to help put researchers and clinicians on the same page as microarray technology moves into the forefront of craniofacial biology.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.205
GPT teacher head0.479
Teacher spread0.273 · 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 designOther design
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

Citations13
Published2007
Admission routes1
Has abstractyes

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