Epilepsy genetics: Current knowledge, applications, and future directions
Bibliographic record
Abstract
The rapid pace of disease gene discovery has resulted in tremendous advances in the field of epilepsy genetics. Clinical testing with comprehensive gene panels, exomes, and genomes are now available and have led to higher diagnostic rates and insights into the underlying disease processes. As such, the contribution to the care of patients by medical geneticists, neurogeneticists and genetic counselors are significant; the dysmorphic examination, the necessary pre- and post-test counseling, the selection of the appropriate next-generation sequencing-based test(s), and the interpretation of sequencing results require a care provider to have a comprehensive working knowledge of the strengths and limitations of the available testing technologies. As the underlying mechanisms of the encephalopathies and epilepsies are better understood, there may be opportunities for the development of novel therapies based on an individual's own specific genotype. Drug screening with in vitro and in vivo models of epilepsy can potentially facilitate new treatment strategies. The future of epilepsy genetics will also probably include other-omic approaches such as transcriptomes, metabolomes, and the expanded use of whole genome sequencing to further improve our understanding of epilepsy and provide better care for those with the disease.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".