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Record W2004922200 · doi:10.2217/bmm.10.68

Chromatin Conformation Signatures: Ideal Human Disease Biomarkers?

2010· review· en· W2004922200 on OpenAlexaff
Jennifer Lynn Crutchley, Xue Qing David Wang, Maria Ferraiuolo, Josée Dostie

Bibliographic record

VenueBiomarkers in Medicine · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsChromatinChromosome conformation captureGenomeHuman genomeGeneChIA-PETComputational biologyGeneticsGene expressionDNAChromosomeBiologyIdeal (ethics)Evolutionary biologyChromatin remodelingEnhancer

Abstract

fetched live from OpenAlex

Human health is related to information stored in our genetic code, which is highly variable even amongst healthy individuals. Gene expression is orchestrated by numerous control elements that may be located anywhere in the genome, and can regulate distal genes by physically interacting with them. These DNA contacts can be mapped with the chromosome conformation capture and related technologies. Several studies now demonstrate that gene expression patterns are associated with specific chromatin structures, and may therefore correlate with chromatin conformation signatures. Here, we present an overview of genome organization and its relationship with gene expression. We also summarize how chromatin conformation signatures can be identified and discuss why they might represent ideal biomarkers of human disease in such genetically diverse populations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.315
Teacher spread0.298 · 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
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

Citations43
Published2010
Admission routes1
Has abstractyes

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