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Record W1563797194 · doi:10.1002/humu.22837

GeneMatcher Aids in the Identification of a New Malformation Syndrome with Intellectual Disability, Unique Facial Dysmorphisms, and Skeletal and Connective Tissue Abnormalities Caused by De Novo Variants in<i>HNRNPK</i>

2015· article· en· W1563797194 on OpenAlexafffund
Ping Yee Billie Au, Jing You, Oana Caluseriu, Jeremy Schwartzentruber, Jacek Majewski, François P. Bernier, Marcia Ferguson, David Valle, Jillian S. Parboosingh, Nara Sobreira, A. Micheil Innes, Antonie D. Kline

Bibliographic record

VenueHuman Mutation · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsAlberta Children's HospitalMcGill UniversityUniversity of AlbertaUniversity of Calgary
FundersNational Human Genome Research InstituteCanadian Institutes of Health ResearchOntario Genomics InstituteGenome Canada
KeywordsBiologyProbandGeneticsIntellectual disabilityExome sequencingFrameshift mutationGenetic heterogeneityKabuki syndromePhenotypeIdentification (biology)Facial dysmorphismCraniofacialBioinformaticsGeneComputational biologyMutation

Abstract

fetched live from OpenAlex

We report a new syndrome due to loss-of-function variants in the heterogeneous nuclear ribonucleoprotein K gene (HNRNPK). We describe two probands: one with a de novo frameshift (NM_002140.3: c.953+1dup), and the other with a de novo splice donor site variant (NM_002140.3: c.257G>A). Both probands have intellectual disability, a shared unique craniofacial phenotype, and connective tissue and skeletal abnormalities. The identification of this syndrome was made possible by a new online tool, GeneMatcher, which facilitates connections between clinicians and researchers based on shared interest in candidate genes. This report demonstrates that new Web-based approaches can be effective in helping investigators solve exome sequencing projects, and also highlights the newer paradigm of "reverse phenotyping," where characterization of syndromic features follows the identification of genetic variants.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.275
Teacher spread0.256 · 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 designBench or experimental
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

Citations70
Published2015
Admission routes2
Has abstractyes

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