{"id":"W4408083017","doi":"10.1101/2025.02.25.640181","title":"geneRNIB: a living benchmark for gene regulatory network inference","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Mila - Quebec Artificial Intelligence Institute; Université de Montréal","funders":"","keywords":"Inference; Benchmark (surveying); Gene regulatory network; Computational biology; Gene; Computer science; Biology; Genetics; Artificial intelligence; Gene expression; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01250812,0.002363252,0.001402647,0.003319166,0.001350301,0.00278523,0.004868833,0.002270341,0.003589647],"category_scores_gemma":[0.02913131,0.0007729247,0.001727252,0.003302268,0.001635942,0.002223029,0.002799998,0.002924046,0.001671094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002756512,"about_ca_system_score_gemma":0.003165216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01353373,"about_ca_topic_score_gemma":0.01297269,"domain_scores_codex":[0.9926832,0.003216976,0.0003979489,0.001513256,0.00187639,0.0003122313],"domain_scores_gemma":[0.9901365,0.005569822,0.0005334037,0.001980145,0.001415263,0.0003649212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008545159,0.000432023,0.01244668,0.002320977,0.001094613,0.0002949568,0.0002445695,0.7521474,0.01063621,0.02785565,0.09138912,0.1002832],"study_design_scores_gemma":[0.0001669888,0.0002480854,0.002554459,0.0002147925,0.00009890708,0.0001382293,0.00008338572,0.935079,0.009445984,0.02706779,0.02482095,0.0000813603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.1492035,0.01235757,0.6552733,0.003537149,0.001659545,0.0007612398,0.05596881,0.09951343,0.02172535],"genre_scores_gemma":[0.3793083,0.002396418,0.4942464,0.001645648,0.0002169433,0.001203792,0.1102071,0.008193173,0.002582134],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01353373,"threshold_uncertainty_score":0.06615007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00980508437019819,"score_gpt":0.2261701165698031,"score_spread":0.2163650321996049,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}