{"id":"W4413334764","doi":"10.1101/2025.08.14.670294","title":"Causal single-cell RNA-seq simulation, in silico perturbation, and GRN inference benchmarking using GRouNdGAN-Toolkit","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"In silico; Benchmarking; Computational biology; Inference; RNA-Seq; Computer science; RNA; Biology; Artificial intelligence; Transcriptome; Genetics; Gene; Gene expression; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002638909,0.001330671,0.0009581922,0.0007795384,0.0006628566,0.001113274,0.002917042,0.001279356,0.01113508],"category_scores_gemma":[0.006180652,0.0007154366,0.00153046,0.0005579827,0.001022402,0.000879057,0.001696987,0.002487644,0.002708217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077388,"about_ca_system_score_gemma":0.001836798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008462307,"about_ca_topic_score_gemma":0.01174407,"domain_scores_codex":[0.999253,0.0002643111,0.00005249026,0.0001877627,0.0001583611,0.00008402731],"domain_scores_gemma":[0.9973104,0.001854842,0.00009508534,0.0003761326,0.0002482777,0.0001153359],"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":[0.000368153,0.0001363249,0.00710258,0.001328356,0.0003073986,0.0003897518,0.0002485113,0.9185351,0.007616839,0.01583319,0.02513102,0.0230027],"study_design_scores_gemma":[0.00005936303,0.00006638688,0.0009176328,0.00006280542,0.00002671769,0.00006161697,0.00003529636,0.9706497,0.006335666,0.01356283,0.008172235,0.00004974817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1164792,0.001226868,0.7006328,0.001316639,0.0005400901,0.0005892594,0.03066096,0.1350454,0.01350881],"genre_scores_gemma":[0.455706,0.00080437,0.4774515,0.001173528,0.00008780017,0.002183895,0.04097661,0.0158907,0.005725457],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01113508,"threshold_uncertainty_score":0.03725052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207323466458371,"score_gpt":0.2414067429093224,"score_spread":0.2193335082447387,"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."}}