{"id":"W7110883209","doi":"10.5281/zenodo.15616830","title":"Data for Exploiting fluctuations in gene expression to detect causal interactions between genes","year":2025,"lang":"","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Gene expression; Expression (computer science); Gene; Segmentation; Synthetic data","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":[0.000729126,0.002294889,0.001158008,0.001291352,0.000721205,0.001162088,0.002721456,0.002871105,0.03707767],"category_scores_gemma":[0.002770992,0.0007445531,0.001397785,0.002108717,0.0005571768,0.0009945541,0.001469921,0.002173287,0.05823803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141479,"about_ca_system_score_gemma":0.00136873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01130826,"about_ca_topic_score_gemma":0.02843979,"domain_scores_codex":[0.9993357,0.00006675089,0.00004617055,0.000230995,0.000226089,0.00009417408],"domain_scores_gemma":[0.9984348,0.0003423744,0.0001079374,0.0006010041,0.0003624344,0.0001514273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003017191,0.0001123307,0.002020583,0.0006094946,0.00006190328,0.0000608511,0.00002671784,0.00263333,0.003355979,0.0008328809,0.9824098,0.007574443],"study_design_scores_gemma":[0.0011026,0.0001938216,0.0190785,0.0002705735,0.0001081868,0.0003068983,0.0001420857,0.01577512,0.02010463,0.01103329,0.9317311,0.0001531951],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001400717,0.0001214293,0.001314577,0.0001302642,0.00007798229,0.00002782985,0.9921513,0.00363182,0.001144151],"genre_scores_gemma":[0.002241549,0.0000367028,0.001962943,0.00005340058,0.000008422239,0.00008279555,0.9945205,0.0002635007,0.0008302371],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03707767,"threshold_uncertainty_score":0.1240371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1151886036944678,"score_gpt":0.3448616628077341,"score_spread":0.2296730591132663,"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."}}