ICNMD 2016: Abstract Book for the 14th International Congress on Neuromuscular Diseases, July 5–9, 2016 Toronto, Canada
Bibliographic record
Abstract
Genomic technologies have profoundly changed our ability to uncover the genes underlying a wide range of rare Mendelian diseases.Here I describe three major technological advances in genomic approaches to rare disease diagnosis, and their application to neuromuscular disease.Firstly, I discuss the development of a massive reference panel of "healthy" exomes, the Exome Aggregation Consortium (ExAC) and demonstrate how ExAC data can be used to more effectively fi lter the variants identifi ed in rare disease patients.Secondly, I outline the value of whole-genome sequencing in the discovery of causal variants missed through exome sequencing.Finally, I describe a pilot study on the application of muscle transcriptome sequencing (RNA-seq) on a set of over 40 exome-unsolved muscle disease cases, and the high resulting diagnostic yield from discovery of splice-disrupting and expression-altering variants.Finally, I outline several unresolved challenges of genomic diagnosis in rare disease cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.337 | 0.188 |
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".