MétaCan
Menu
← Back to cohort
Record W2220657520 · doi:10.1101/035253

High-accuracy HLA type inference from whole-genome sequencing data

2015· preprint· en· W2220657520 on OpenAlexaff
Alexander Dilthey, Pierre‐Antoine Gourraud, Zamin Iqbal, Gil McVean

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2015
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsDiscovery Centre
FundersInstitut National de la Santé et de la Recherche MédicaleRoyal SocietyWellcome Trust
KeywordsInferenceHuman leukocyte antigenComputer scienceWhole genome sequencingReference genomeGenomeComputational biologyDNA sequencingGeneticsBiologyArtificial intelligenceGene

Abstract

fetched live from OpenAlex

Extensive hyperpolymorphism and sequence similarity between the HLA genes make HLA type inference from whole-genome sequencing data a challenging problem. We address these by representing sequences from over 10,000 known alleles in a reference graph structure, enabling accurate read mapping. HLA*PRG, our algorithm, outperforms existing methods by a wide margin and for the first time consistently achieves the accuracy of gold-standard reference methods with one error across 158 alleles tested.

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)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.044
GPT teacher head0.249
Teacher spread0.206 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicT-cell and B-cell Immunology→French-language works237,207→