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Record W2188890094 · doi:10.1186/s13073-015-0248-x

Profiling tissue-resident T cell repertoires by RNA sequencing

2015· article· en· W2188890094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGenome Medicine · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsSimon Fraser UniversityBC Cancer AgencyCanada's Michael Smith Genome Sciences CentreGenome British ColumbiaUniversity of British Columbia
FundersNational Cancer InstituteNational Human Genome Research InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsT-cell receptorComputational biologyRNABiologyDNA sequencingRepertoireGeneSequence analysisRNA-SeqT cellGeneticsImmune systemTranscriptomeGene expression

Abstract

fetched live from OpenAlex

Deep sequencing of recombined T cell receptor (TCR) genes and transcripts has provided a view of T cell repertoire diversity at an unprecedented resolution. Beyond profiling peripheral blood, analysis of tissue-resident T cells provides further insight into immune-related diseases. We describe the extraction of TCR sequence information directly from RNA-sequencing data from 6738 tumor and 604 control tissues, with a typical yield of 1 TCR per 10 million reads. This method circumvents the need for PCR amplification of the TCR template and provides TCR information in the context of global gene expression, allowing integrated analysis of extensive RNA-sequencing data resources.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.999

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)0.0030.002

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 head0.027
GPT teacher head0.254
Teacher spread0.226 · 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