{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001078011,0.0003755699,0.0004266618,0.00007702847,0.0008011471,0.00005599921,0.000789432,0.0002170133,0.0003722708],"category_scores_gemma":[0.0001103336,0.0003978478,0.0002841843,0.0009244818,0.0002574892,0.000009933957,0.001032683,0.000399388,0.00003416702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009451182,"about_ca_system_score_gemma":0.0001724732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002826928,"about_ca_topic_score_gemma":0.00001011086,"domain_scores_codex":[0.9966089,0.000457788,0.0006783181,0.001025399,0.0003201245,0.0009094912],"domain_scores_gemma":[0.9981495,0.00007863357,0.0002952614,0.00114013,0.0001336384,0.0002028704],"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":[0.00004408071,0.0001105605,0.008927812,0.000008988359,0.00008107307,0.000008832516,0.00006454089,0.9270602,0.05924897,0.000965519,0.001110473,0.002368961],"study_design_scores_gemma":[0.0001428752,0.0005355512,0.001574604,0.00003292789,0.0001271964,0.0001757957,0.0005735807,0.9173013,0.05288233,0.001522781,0.02385789,0.001273141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7408226,0.002941797,0.254007,0.00004407694,0.0004987892,0.0003671941,0.00001613694,0.00006034829,0.001241961],"genre_scores_gemma":[0.9898154,0.0002272442,0.00730507,0.0002479257,0.00112445,0.00009350299,0.0002433392,0.00004811755,0.0008949622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2489927,"threshold_uncertainty_score":0.9998474,"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."}}