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Record W6968518956 · doi:10.5281/zenodo.16782450

Borzoi Human Model Predictor (Linder et al. 2025) using the Genomic API for Model Evaluation (GAME) Framework

2025· other· en· W6968518956 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContainer (type theory)Scripting languageENCODEReplicateSource codeCode (set theory)Sample (material)Interpretation (philosophy)

Abstract

fetched live from OpenAlex

This record provides a Predictor container for the Borzoi human model (Linder et al. 2025, Nature Genetics). Borzoi is a deep learning model that predicts cell-type and tissue-specific RNA-seq coverage directly from DNA sequence, enabling interpretation of genetic variants across multiple layers of gene regulation, including transcription, splicing, and polyadenylation. It is trained on human RNA-seq data from ENCODE (with 866 datasets across diverse biosamples, including cell lines and adult tissues) and Genotype-Tissue Expression (GTEx) data (with 2-3 replicates for each tissue, processed by the recount3 project). The training dataset also includes epigenomic datasets from the Enformer model, such as CAGE, DNase-seq, ATAC-seq, and ChIP-seq tracks. The Predictor container (borzoi_human_predictor.sif) includes: Predictor script for sequence processing and error handling. Integrated Borzoi model with its dependencies and borzoi-gpu conda environment created using borzoi_gpu_environment.yml. Baskerville and Borzoi packages, installed from local source copies of repositories (as of March 2025), which also contain helper scripts and 4 replicate model weights. Support scripts like: api_preprocessing_utils.py, error_message_functions_updated.py, predictor_help_message.json. NOTE: This container requires a GPU for execution because the Borzoi model relies on TensorFlow's GPU-accelerated operations. Running the container: Ensure Apptainer is intalled in the system the container is intended to run. Always run the Predictor first, so it can listen for incoming connections from Evaluators: apptainer run --containall --nv borzoi_human_predictor.sif HOST_IP HOST_PORT MATCHER_IP MATCHER_PORT Additional information about the GAME framework can be found on GitHub: Genomic API for Model Evaluation Borzoi Human Predictor-specific information, along with a sample Evaluator, can be found within the same repository: Borzoi Predictor Code repository for Borzoi models: Borzoi GitHub Paper can be found here: Linder, J., Srivastava, D., Yuan, H. et al. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nat Genet 57, 949–961 (2025). https://doi.org/10.1038/s41588-024-02053-6

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.318
Teacher spread0.245 · 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
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
Published2025
Admission routes1
Has abstractyes

Explore more

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