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Record W4400493337 · doi:10.2139/ssrn.4890101

Mechanism-Free Repurposing of Drugs For C9orf72-Related ALS/FTD Using Large-Scale Genomic Data

2024· preprint· en· W4400493337 on OpenAlexaff
Sara Sáez-Atiénzar, Cleide Dos Santos Souza, Ruth Chia, Selina N. Beal, Ileana Lorenzini, Ruili Huang, Jennifer Lévy, Camelia Burciu, Jinhui Ding, J. Raphael Gibbs, Ashley Jones, Ramita Dewan, Viviana Pensato, Silvia Peverelli, Lucia Corrado, Joke J.F.A. van Vugt, Wouter van Rheenen, Ceren Tunca, Elif Bayraktar, Menghang Xia, Alfredo Iacoangeli, Aleksey Shatunov, Cinzia Tiloca, Nicola Ticozzi, Federico Verde, Letizia Mazzini, Kevin P. Kenna, Ahmad Al Khleifat, Sarah Opie-Martin, Flavia Raggi, Massimiliano Filosto, Stefano Cotti Piccinelli, Alessandro Padovani, Stella Gagliardi, Maurizio Inghilleri, Alessandra Ferlini, Rosario Vasta, Andrea Calvo, Cristina Moglia, Antonio Canosa, Umberto Manera, Maurzio Grassano, Jessica Mandrioli, Gabriele Mora, Christian Lunetta, Raffaella Tanel, Francesca Trojsi, Patrizio Cardinali, Salvatore Gallone, Maura Brunetti, Daniela Galimberti, María Serpente, Chiara Fenoglio, Elio Scarpini, Giacomo P. Comi, Stefania Corti, Roberto Del Bo, Mauro Ceroni, Giuseppe Lauria, Franco Taroni, Eleonora Dalla Bella, Enrica Bersano, Charles Curtis, Sang Hyuck Lee, Raymond T. Chung, Hamel Patel, Karen Morrison, Johnathan Cooper‐Knock, Pamela J. Shaw, Gerome Breen, Richard Dobson, Clifton L. Dalgard, Sonja W. Scholz, Ammar Al‐Chalabi, Leonard van den Berg, Russell L. McLaughlin, Orla Hardiman, Cristina Cereda, Gianni Sorarú, Sandra D apos Alfonso, Siddharthan Chandran, Suvankar Pal, Antonia Ratti, Cinzia Gellera, Kory R. Johnson, Tara Doucet-O apos Hare, Nicholas Pasternack, Tongguang Wang, Avindra Nath, Gabriele Siciliano, Vincenzo Silani, A. Nazlı Başak, Jan H. Veldink, William Camu, Jonathan D. Glass, John E. Landers, Adriano Chiò, Rita Sattler, Christopher E. Shaw, Laura Ferraiuolo, Isabella Fogh, Bryan J. Traynor

Bibliographic record

VenueSSRN Electronic Journal · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsRepurposingMechanism (biology)C9orf72Scale (ratio)Drug repositioningComputational biologyComputer sciencePharmacologyMedicineBiologyFrontotemporal dementiaDrugInternal medicineDementiaPhysics

Abstract

fetched live from OpenAlex
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 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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.025
GPT teacher head0.310
Teacher spread0.285 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractno

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