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Application of Exome Sequencing to Mendelian Disorders and the Emergence of Personalised Medicine

2013· other· en· W1918008698 on OpenAlexaff
Mikolaj Raszek

Bibliographic record

VenueEncyclopedia of Life Sciences · 2013
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMendelian inheritanceExome sequencingDiseasePersonalized medicineGenomicsExomeBiologyPrecision medicineComputational biologyDNA sequencingGeneticsBioinformaticsMutationMedicineGenomeGene

Abstract

fetched live from OpenAlex

Abstract Since the introduction of next‐generation sequencing, the field of genomic medicine has expanded rapidly. Whole‐exome sequencing (WES) can sequence thousands of functional genes at a time and has become the tool of choice for the discovery of causative genetic variants, especially for Mendelian diseases. This approach also expands the knowledge of novel mutations of established genes linked to a particular disease, and helps to uncover the complex interplay between modifier variants that contribute to a disease phenotype. For these reasons, WES can perform diagnoses that would be difficult to accomplish with traditional methods, particularly for diseases that exhibit a broad genetic or phenotypic heterogeneity. Together, these trends already have begun to influence the quality of patient care and appear to fulfil the promise of personal medicine. The article summarises recent literature that highlights these trends, and discusses the limitations and ethical considerations surrounding this new technology. Key Concepts: Next‐generation sequencing is driving the emergence of personalised medicine. Superiority of WES as a diagnostic tool over traditional genetic methods is apparent, especially for diseases that are genetically and phenotypically heterogenous. WES utility is a rapidly expanding field of genetic aetiology. The underlying genetic complexity of monogenic diseases might have been underappreciated. The clinical manifestation of monogenic diseases is driven by a complex interplay of a multitude of genetic modifiers. The incidentalome remains an important ethical issue, but important steps forward have been taken with the work of Dr. Berg and collaborators commencing in 2011. The rapidly expanding field of genomics faces important technical limitations: storage space and analysis capability. Important ethical questions need to be addressed with regard to the use of NGS technology; this includes rights to data access and issues regarding follow‐up data reanalysis.

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)0.039
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.066
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.008
Scholarly communication0.0040.006
Open science0.0010.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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