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Record W2150904790 · doi:10.1038/459927a

Unlocking the secrets of the genome

2009· article· en· W2150904790 on OpenAlexfundno aff
S Celniker, Laura A. L. Dillon, Mark Gerstein, Kristin C. Gunsalus, Steven Henikoff, Gary H. Karpen, Manolis Kellis, Eric C. Lai, Jason D. Lieb, David M. MacAlpine, Gos Micklem, Fabio Piano, M Snyder, Lincoln Stein, Kevin P. White, R Waterston

Bibliographic record

VenueNature · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryNational Institute of General Medical SciencesNational Human Genome Research InstituteComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyYork UniversityArgonne National LaboratoryUniversity of North Carolina at Chapel HillBroad InstituteMassachusetts Institute of TechnologyUniversity of WashingtonHarvard UniversityNational Institutes of HealthYale University
KeywordsCaenorhabditis elegansGenomeMulticellular organismOrganismModel organismBiologyGenomicsDrosophila melanogasterEncyclopediaComputational biologyKey (lock)Plan (archaeology)Data scienceEvolutionary biologyGeneticsComputer scienceEcologyGeneLibrary science

Abstract

fetched live from OpenAlex

Despite the successes of genomics, little is known about how genetic information produces complex organisms. A look at the crucial functional elements of fly and worm genomes could change that. The National Human Genome Research Institute's modENCODE project (the model organism ENCyclopedia Of DNA Elements) was set up in 2007 with the goal of identifying all the sequence-based functional elements in the genomes of two important experimental organisms, Caenorhabditis elegans and Drosophila melanogaster. Armed with modENCODE data, geneticists will be able to undertake the comprehensive molecular studies of regulatory networks that hold the key to how complex multicellular organisms arise from the list of instructions coded in the genome. In this issue, modENCODE team members outline their plan of campaign. Data from the project are to be made available on http://www.modencode.org and elsewhere as the work progresses.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0050.010
Open science0.0010.002
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0090.004

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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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
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

Citations817
Published2009
Admission routes1
Has abstractyes

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