{"id":"W4220836239","doi":"10.18280/ria.360116","title":"Biological Network, Gene Regulatory Network Inference Using Causal Inference Approach","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Gene regulatory network; Causal inference; Computer science; Granger causality; Artificial intelligence; Cluster analysis; Computational biology; Causality (physics); Machine learning; Data mining; Gene; Biology; Mathematics; Econometrics; Gene expression; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002421653,0.0008263596,0.000626935,0.00315295,0.0007319692,0.001200521,0.0011757,0.001080092,0.004312017],"category_scores_gemma":[0.008544574,0.0003459159,0.001647745,0.002910675,0.0007377857,0.001651918,0.0006902174,0.001665428,0.0007109034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129233,"about_ca_system_score_gemma":0.001567303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006390452,"about_ca_topic_score_gemma":0.007912572,"domain_scores_codex":[0.998408,0.0006769376,0.00008968091,0.0005063539,0.0002638008,0.00005530268],"domain_scores_gemma":[0.9962195,0.002852167,0.000308225,0.0003400149,0.0002171012,0.00006310827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00029456,0.0002772466,0.02194156,0.001616327,0.0009055861,0.0008786386,0.0002497671,0.4450137,0.005397493,0.1823615,0.01437001,0.3266937],"study_design_scores_gemma":[0.00003158155,0.00005105379,0.00233654,0.00007890312,0.0001351149,0.0002843188,0.0000784037,0.7599265,0.002087557,0.2204203,0.01454428,0.00002552905],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01085417,0.001595093,0.9789467,0.0008427694,0.00007820204,0.0001424438,0.003895912,0.001194213,0.002450552],"genre_scores_gemma":[0.3367747,0.002611858,0.6467042,0.0005754689,0.0001952618,0.0003988303,0.00932523,0.0001831019,0.003231295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006390452,"threshold_uncertainty_score":0.0144251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05703095590954063,"score_gpt":0.2818952079151489,"score_spread":0.2248642520056083,"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."}}