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Genome-wide Analyses Identify KIF5A as a Novel ALS Gene
Aude Nicolas, Kevin P. Kenna, Alan E. Renton, Nicola Ticozzi, Faraz Faghri, Ruth Chia, Janice A. Dominov, Brendan Kenna, Mike A. Nalls, Pamela Keagle, Alberto Rivera, Natalie A. Murphy, Joke J.F.A. van Vugt, Joshua T. Geiger, Rick A. A. van der Spek, Hannah A. Pliner, Shankaracharya, Bradley Smith, Simon Topp, Yevgeniya Abramzon, Soragia Athina Gkazi, John D. Eicher, Aoife Kenna, Isabella Laura Simone, Giancarlo Logroscino, Ilaria Bartolomei, Maria Rita Murru, Carla Pani, Roberta Puddu, Carla Caredda, Valeria Piras, Stefania Tranquilli, Stefania Cuccu, Daniela Corongiu, Maurizio Melis, Antonio Milia, Francesco Marrosu, Maria Giovanna Marrosu, Gianluca Floris, Antonino Cannas, Margherita Capasso, Claudia Caponnetto, Gianluigi Mancardi, Paola Origone, Paola Mandich, F. L. 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Bibliographic record
VenueNeuron · 2018
Typearticle
Languagede
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsSunnybrook Health Science CentreMontreal Neurological Institute and HospitalUniversity of TorontoMcGill UniversityHealth Sciences CentreOccupational Cancer Research Centre
FundersNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Health and Medical Research CouncilMedical Research CouncilNational Institute on Minority Health and Health DisparitiesRegione PiemonteUniversity College London Hospitals NHS Foundation TrustNeuroförbundetUK Dementia Research InstituteFondazione Italiana di Ricerca per la Sclerosi Laterale AmiotroficaKnut och Alice Wallenbergs StiftelseFonds Wetenschappelijk OnderzoekVästerbotten Läns LandstingNational Institutes of HealthRosetrees TrustMassachusetts Institute of TechnologyEuropean CommissionNational Heart, Lung, and Blood InstituteAcademy of Medical SciencesNational Institute on AgingHealth Research BoardNational Institute for Health and Care ResearchConsortium canadien en neurodégénérescence associée au vieillissementUlla-Carin Lindquists stiftelse för ALS-forskningMotor Neurone Disease AssociationKU LeuvenRare Diseases Clinical Research NetworkNew York Genome CenterScience Foundation IrelandVlaamse regeringTow FoundationTarget ALSMinistero della SaluteAlzheimer's SocietyALS AssociationWellcome TrustTorsten Söderbergs StiftelseU.S. Department of Veterans AffairsUniversità degli Studi di TorinoCytokineticsALS Therapy AllianceMuscular Dystrophy AssociationMotor Neurone Disease Research Institute of Australia
KeywordsGeneGenomeBiologyComputational biologyGenetics
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 head0.118
GPT teacher head0.407
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Citations718
Published2018
Admission routes2
Has abstractno