MétaCan
Menu
Back to cohort
Record W2596607470 · doi:10.21037/tau.2017.03.06

Long non-coding RNAs: new frontiers for advancing personalized cancer medicine in prostate cancer

2017· letter· en· W2596607470 on OpenAlexafffund
Alireza Fotouhi Ghiam, Danny Vesprini, Stanley K. Liu

Bibliographic record

VenueTranslational Andrology and Urology · 2017
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersProstate Cancer Fight FoundationUniversity of TorontoMovember Foundation
KeywordsProstate cancerCoding (social sciences)CancerPersonalized medicineCancer MedicinePrecision medicineComputational biologyMedicinemicroRNABioinformaticsBiologyInternal medicineGeneticsGenePathology

Abstract

fetched live from OpenAlex

Long non-coding RNAs (lncRNAs) are a group of non-coding transcripts of more than 200 nucleotides that play important biological and clinical roles in prostate cancer (PCa) tumorigenesis, progression and metastasis. They have also shown potential as a biomarker in the diagnosis and prognosis of this disease. LncRNA prostate cancer associated transcript-14 (PCAT-14) was recently identified as a novel prognostic biomarker in PCa, whose low expression was associated with poor outcomes. Here, we briefly discuss future perspectives and clinical applications of lncRNAs as biomarkers and therapeutic targets for PCa.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.307
Teacher spread0.293 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations11
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueTranslational Andrology and UrologySame topicCancer-related molecular mechanisms researchFrench-language works237,207