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Record W2416454361 · doi:10.1007/978-1-62703-589-7_19

Genome-Based Bioinformatic Prediction of Major Histocompatibility Complex (MHC) Epitopes

2013· article· en· W2416454361 on OpenAlexaff
Simon J. Foote

Bibliographic record

VenueMethods in molecular biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicvaccines and immunoinformatics approaches
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMajor histocompatibility complexComputational biologyGenomeEpitopeBiologyGeneGeneticsAntigen

Abstract

fetched live from OpenAlex

Over the last 12 years, a large amount of knowledge has been accumulated on various aspects of Major Histocompatibility Complex (MHC) molecules. In conjunction, numerous algorithms and tools have been developed to screen protein molecules for these MHC receptor sites. By combining these computational tools and databases with genomic sequence information that is now widely available for a vast range of organisms, it is possible to screen whole genomes for MHC epitopes. By prescreening these genomes, it allows the researcher to narrow down possible protein targets for further analysis by traditional tools such as gene knockouts and animal efficacy studies.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.114
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.323
Teacher spread0.294 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2013
Admission routes1
Has abstractyes

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