{"id":"W2172648302","doi":"10.1007/978-94-017-8896-0_17","title":"Toward Intracellular Delivery and Drug Discovery: Stochastic Logic Networks as Efficient Computational Models for Gene Regulatory Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Fundamental biomedical technologies","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Gene regulatory network; Probabilistic logic; Boolean network; Computer science; Biological network; Theoretical computer science; And-inverter graph; Boolean function; Computational biology; Boolean circuit; Gene; Algorithm; Biology; Artificial intelligence; Gene expression; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003779079,0.0007241456,0.0007687131,0.0002604503,0.0002669827,0.0001068481,0.0005930887,0.001478028,0.00001848528],"category_scores_gemma":[0.00004768179,0.0006711548,0.0004182188,0.00009492024,0.001742388,0.00000777295,0.001002288,0.000459259,0.000007368204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001379057,"about_ca_system_score_gemma":0.0001054694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004527034,"about_ca_topic_score_gemma":0.000002624773,"domain_scores_codex":[0.9968926,0.0000371719,0.0006563353,0.001263728,0.0005088674,0.0006412455],"domain_scores_gemma":[0.9985138,0.0001072029,0.0004034524,0.0006790543,0.0001155152,0.0001809466],"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":[0.0003191755,0.0001257268,0.000008510904,0.0001193092,0.001247806,0.00002939023,0.0000227272,0.9122809,0.002318031,0.03717277,0.009235731,0.03711997],"study_design_scores_gemma":[0.001707674,0.001147918,0.0000118054,0.000347489,0.0007570056,0.0001486012,0.000244974,0.9130499,0.001133557,0.05960023,0.01997576,0.001875083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02149533,0.02768606,0.9479121,0.0005369828,0.00050013,0.001032462,0.0001792654,0.0002899153,0.0003677371],"genre_scores_gemma":[0.9826046,0.001192645,0.003889976,0.0002256902,0.000547254,0.0001073784,0.003059117,0.000120629,0.008252683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9611093,"threshold_uncertainty_score":0.9998183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124696632747513,"score_gpt":0.2152243253908006,"score_spread":0.2039773590633255,"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."}}