What can exome sequencing do for you?
Bibliographic record
Abstract
Recent advances in next-generation sequencing technologies have brought a paradigm shift in how medical researchers investigate both rare and common human disorders. The ability cost-effectively to generate genome-wide sequencing data with deep coverage in a short time frame is replacing approaches that focus on specific regions for gene discovery and clinical testing. While whole genome sequencing remains prohibitively expensive for most applications, exome sequencing--a technique which focuses on only the protein-coding portion of the genome--places many advantages of the emerging technologies into researchers' hands. Recent successes using this technology have uncovered genetic defects with a limited number of probands regardless of shared genetic heritage, and are changing our approach to Mendelian disorders where soon all causative variants, genes and their relation to phenotype will be uncovered. The expectation is that, in the very near future, this technology will enable us to identify all the variants in an individual's personal genome and, in particular, clinically relevant alleles. Beyond this, whole genome sequencing is also expected to bring a major shift in clinical practice in terms of diagnosis and understanding of diseases, ultimately enabling personalised medicine based on one's genome. This paper provides an overview of the current and future use of next generation sequencing as it relates to whole exome sequencing in human disease by focusing on the technical capabilities, limitations and ethical issues associated with this technology in the field of genetics and human disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".