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Record W2978417493 · doi:10.1002/mgg3.859

The CLN3 gene and protein: What we know

2019· review· en· W2978417493 on OpenAlexaff
Myriam Mirza, Anna Vainshtein, Alberto DiRonza, Uma Chandrachud, Luke J. Haslett, Michela Palmieri, Stephan Storch, Janos Groh, Niv Dobzinski, Gennaro Napolitano, Carolin Schmidtke, Danielle Kerkovich

Bibliographic record

VenueMolecular Genetics & Genomic Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsThornhill Medical (Canada)
FundersBeyond Batten Disease Foundation
KeywordsBatten diseaseProduct (mathematics)TrademarkDiseaseData scienceComputer scienceComputational biologyBiologyMedicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: One of the most important steps taken by Beyond Batten Disease Foundation in our quest to cure juvenile Batten (CLN3) disease is to understand the State of the Science. We believe that a strong understanding of where we are in our experimental understanding of the CLN3 gene, its regulation, gene product, protein structure, tissue distribution, biomarker use, and pathological responses to its deficiency, lays the groundwork for determining therapeutic action plans. OBJECTIVES: To present an unbiased comprehensive reference tool of the experimental understanding of the CLN3 gene and gene product of the same name. METHODS: BBDF compiled all of the available CLN3 gene and protein data from biological databases, repositories of federally and privately funded projects, patent and trademark offices, science and technology journals, industrial drug and pipeline reports as well as clinical trial reports and with painstaking precision, validated the information together with experts in Batten disease, lysosomal storage disease, lysosome/endosome biology. RESULTS: The finished product is an indexed review of the CLN3 gene and protein which is not limited in page size or number of references, references all available primary experiments, and does not draw conclusions for the reader. CONCLUSIONS: Revisiting the experimental history of a target gene and its product ensures that inaccuracies and contradictions come to light, long-held beliefs and assumptions continue to be challenged, and information that was previously deemed inconsequential gets a second look. Compiling the information into one manuscript with all appropriate primary references provides quick clues to which studies have been completed under which conditions and what information has been reported. This compendium does not seek to replace original articles or subtopic reviews but provides an historical roadmap to completed works.

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)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.004
Science and technology studies0.0020.006
Scholarly communication0.0070.014
Open science0.0030.002
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0050.004

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 head0.051
GPT teacher head0.354
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations76
Published2019
Admission routes1
Has abstractyes

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