MétaCan
Menu
Back to cohort
Record W4312221621 · doi:10.1016/j.gendis.2022.11.018

The emerging role of snoRNAs in human disease

2022· review· en· W4312221621 on OpenAlexfundno aff
Xinhai Zhang, Chenglong Wang, Shujun Xia, Fei Xiao, Jianping Peng, Yuxuan Gao, Fengbin Yu, Chuandong Wang

Bibliographic record

VenueGenes & Diseases · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong ProvinceBasic Public Welfare Research Program of Zhejiang ProvinceNational Natural Science Foundation of ChinaUniversity of Ottawa
KeywordsSmall nucleolar RNABiologyComputational biologyBiomarkermicroRNALong non-coding RNARNAGeneticsGene

Abstract

fetched live from OpenAlex

Small nucleolar RNAs (snoRNAs) play critical roles in various biological processes. The aberrant expression or depletion of snoRNAs is related to various diseases. In previous research, most of the snoRNAs were categorized as C/D box snoRNAs and H/ACA box snoRNAs, whose typical functions were thought of as regulation of 2'-O-ribose methylation and pseudouridylation of ribosome RNAs, respectively. However, in the past two decades, studies have revealed an increasing number of snoRNAs without specific targets or determined cell functions. These findings indicated that some potential roles of snoRNAs are still unknown. Numerous studies have indicated the correlation of snoRNAs with human diseases. SnoRNAs play various roles in abundant biological processes, and they have great potential in controlling human diseases. This new and rising field could benefit from investigations of the disease pathogenesis, biomarker identification, and the determination of novel therapeutic targets. This review summarized the reports on snoRNAs and the regulation of different diseases in recent years.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.998
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.328
Teacher spread0.305 · 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 teacher head, 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

Citations51
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGenes & DiseasesSame topicRNA modifications and cancerFrench-language works237,207