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Record W3095579319 · doi:10.1101/2020.10.26.20191510

Assessment of <i>ANG</i> variants in Parkinson’s disease

2020· preprint· en· W3095579319 on OpenAlexaff
Francis P. Grenn, Anni Moore, Sara Bandrés‐Ciga, Lynne Krohn, Cornelis Blauwendraat

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Institute on AgingVerily Life SciencesCelgenePfizerNational Institutes of HealthU.S. Department of Health and Human ServicesMichael J. Fox Foundation for Parkinson's ResearchFoundation for the National Institutes of HealthU.S. Department of Defense
KeywordsAngiogeninAmyotrophic lateral sclerosisParkinson's diseaseDiseaseLRRK2NeuroprotectionGenetic associationMedicineGenotypeBioinformaticsSingle-nucleotide polymorphismGeneGeneticsBiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Genetic risk factors are occasionally shared between different neurodegenerative diseases. Previous studies have linked ANG , a gene encoding angiogenin, to both Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Functional studies suggest ANG plays a neuroprotective role in both PD and ALS by reducing cell death. We further explored the genetic association between ANG and PD by analyzing genotype data from the International Parkinson’s Disease Genomics Consortium (IPDGC) (14,671 cases and 17,667 controls) and whole genome sequencing (WGS) data from the Accelerating Medicines Partnership - Parkinson’s disease initiative (AMP-PD, https://amp-pd.org/ ) (1,647 cases and 1,050 controls). Our analysis did not replicate the findings of previous studies and found no significant association between ANG variants and PD risk.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.351
Teacher spread0.295 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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Same venuemedRxiv→Same topicAmyotrophic Lateral Sclerosis Research→French-language works237,207→