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Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures

2012· article· en· W2153595490 on OpenAlexfundno aff
Tian Ge, Jianfeng Feng, Derrek P. Hibar, Paul M. Thompson, Thomas E. Nichols

Bibliographic record

VenueNeuroImage · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsnot available
FundersJohnson and Johnson Pharmaceutical Research and DevelopmentJanssen Research and DevelopmentNational Institute of Biomedical Imaging and BioengineeringNational Institute of Mental HealthNational Institute on AgingGenentechNational Institutes of HealthServierInnogeneticsEli Lilly and CompanyChina Scholarship CouncilEisaiEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAlzheimer's Drug Discovery FoundationChinese Academy of SciencesPfizerBiogenBioClinicaEngineering and Physical Sciences Research CouncilBayer HealthCareJanssen Alzheimer Immunotherapy Research And DevelopmentEuropean CommissionAlzheimer's Disease Neuroimaging InitiativeMedical Research CouncilJohnson and JohnsonMeso Scale DiagnosticsNational Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of SciencesMedpaceIC Design Education CenterRoyal SocietyBristol-Myers SquibbAstraZenecaNovartis Pharmaceuticals CorporationF. Hoffmann-La RocheAlzheimer's AssociationAmorfix Life SciencesMerckNational Science Foundation
KeywordsVoxelLocus (genetics)Computer scienceGenome-wide association studyPermutation (music)Single-nucleotide polymorphismArtificial intelligenceKernel (algebra)Pattern recognition (psychology)NeuroimagingRandom permutationMultiple comparisons problemComputational biologyMathematicsBiologyGeneticsStatisticsGeneNeuroscienceGenotype

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations94
Published2012
Admission routes1
Has abstractno

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