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Evaluation of the Disease Liability of CFTR Variants

2011· article· en· W187179976 on OpenAlexaff
Patrick R. Sosnay, Carlo Castellani, Mary Corey, Ruslan Dorfman, Julian Zielenski, Rachel Karchin, Christopher M. Penland, Garry R. Cutting

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteCystic Fibrosis Foundation
KeywordsIn silicoMutationDiseaseGeneticsComputational biologyGeneBiologyBioinformaticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

Over 1600 novel sequence variants in the CFTR gene have been reported to the CF Mutation Database (http://www.genet.sickkids.on.ca/cftr/Home.html). While about 25 mutations are well characterized by clinical studies and functional assays, the disease liability of most of the remaining mutations is either unclear or unknown. This gap in knowledge has implications for diagnosis, therapy selection, and counseling for patients and families carrying an uncharacterized CFTR mutation. This chapter will describe a critical approach to assessing the disease implications of CFTR mutations utilizing clinical data, literature review, functional testing, and bioinformatic in silico methods.

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)0.006
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.470
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.015
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 head0.106
GPT teacher head0.498
Teacher spread0.392 · 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.

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

Citations31
Published2011
Admission routes1
Has abstractyes

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