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Record W3194172494 · doi:10.1186/s12920-021-01066-y

Exome sequencing identifies novel and known mutations in families with intellectual disability

2021· article· en· W3194172494 on OpenAlexaff
Memoona Rasheed, Valeed Khan, Ricardo Harripaul, Madiha Amin Malik, Zahid Ullah, Muhammad Zahid, John B. Vincent, Muhammad Ansar

Bibliographic record

VenueBMC Medical Genomics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersHigher Education Commision, Pakistan
KeywordsHuman geneticsExome sequencingIntellectual disabilityGeneticsBiologyComputational biologyExomeMutationDNA sequencingBioinformaticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: Intellectual disability (ID) is a phenotypically and genetically heterogeneous disorder. METHODS: In this study, genome wide SNP microarray and whole exome sequencing are used for the variant identification in eight Pakistani families with ID. Beside ID, most of the affected individuals had speech delay, facial dysmorphism and impaired cognitive abilities. Repetitive behavior was observed in MRID143, while seizures were reported in affected individuals belonging to MRID137 and MRID175. RESULTS: In two families (MRID137b and MRID175), we identified variants in the genes CCS and ELFN1, which have not previously been reported to cause ID. In four families, variants were identified in ARX, C5orf42, GNE and METTL4. A copy number variation (CNV) was identified in IL1RAPL1 gene in MRID165. CONCLUSION: These findings expand the existing knowledge of variants and genes implicated in autosomal recessive and X linked ID.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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