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Record W4394440558 · doi:10.6084/m9.figshare.19351125

Additional file 3 of Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles

2022· dataset· en· W4394440558 on OpenAlexaff
Chengyou Liu, Andrew M. Hogan, Hunter Sturm, Mohd Wasif Khan, Md. Mohaiminul Islam, A. S. M. Zisanur Rahman, Rebecca L. Davis, Silvia T. Cardona, Pingzhao Hu

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsCancerCare ManitobaUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsMechanism (biology)Scale (ratio)Computer scienceArtificial intelligenceAction (physics)Mechanism of actionComputational biologyMachine learningBiologyGeneticsCartographyGeographyPhysics

Abstract

fetched live from OpenAlex

Additional file 3: Table S2. Annotation of the M. Tuberculosis genes and clusters. M. tuberculosis genes are represented by gene symbols and the protein RefSeq ID and COG category eggNOG.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.503
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5030.110

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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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