MétaCan
Menu
Back to cohort
Record W2388084951

The progress of phenylalanine hydroxylase gene mutations as well as relationship between genotype and phenotype.

2010· article· en· W2388084951 on OpenAlexaboutno aff
Feng Hui-ge

Bibliographic record

VenueChinese Journal of Birth Health & Heredity · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPhenylalanine hydroxylaseHyperphenylalaninemiaPhenotypePhenylalanineGenotypeAlleleGeneGeneticsTyrosine hydroxylaseBiologyTyrosineInborn error of metabolismMutationPhenylketonuriasEnzymeGene mutationGenotype-phenotype distinctionBiochemistryAmino acid
DOInot available

Abstract

fetched live from OpenAlex

Phenylketonuria(PKUMIM#261600) is the most common inborn error of amino acid metabolism in many countries.It is transmitted in autosomal-recessive pattern.PKU is caused by deficiency of hepatic enzyme phenylalanine hydroxylase(PAHEC 1.14.16.1)which catalyses the conversion of phenylalanine to tyrosine.Defects in PAH enzyme result in the elevated serum level of phenylalanine and mental retardation.The hyperphenylalaninemia phenotype is highly variable ranging from mild hyperphenylalaninemia(MHP) to the most severe form classic PKU.At present 546 mutative alleles and 659 genotypes were found in the worldwhich catalogued in PAHdb database(http//www.pahdb.mcgill.ca).Although phenotype is closely related to genotype several different patients who carried the same mutations are not consistency as to phenotype.this article collected research accomplishments reported in recent years and detailed the aspects including pah gene characteristics PAH enzyme structure gene mutation as well as the relationship between genotype and phenotype.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.300
Teacher spread0.291 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueChinese Journal of Birth Health & HereditySame topicMetabolism and Genetic DisordersFrench-language works237,207