{"id":"W2070718329","doi":"10.1142/s0219720004000892","title":"IMPROVING GENE NETWORK INFERENCE BY COMPARING EXPRESSION TIME-SERIES ACROSS SPECIES, DEVELOPMENTAL STAGES OR TISSUES","year":2004,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université de Montréal","funders":"","keywords":"Inference; Gene regulatory network; Computer science; Graph; Time series; Series (stratigraphy); Gene; Data mining; Computational biology; Gene expression; Biology; Artificial intelligence; Machine learning; Theoretical computer science; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002357575,0.0001437386,0.0002564093,0.00003753928,0.0001724081,0.00005744811,0.0001496168,0.0001042884,0.00001321622],"category_scores_gemma":[0.00003886393,0.0001078994,0.00005812121,0.00008502609,0.0001282854,0.00002497407,0.0001697949,0.00008679581,0.00000309609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002884547,"about_ca_system_score_gemma":0.0001576637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003134055,"about_ca_topic_score_gemma":0.000006603056,"domain_scores_codex":[0.9989986,0.00002769798,0.0005298245,0.0001062447,0.0001223415,0.0002153023],"domain_scores_gemma":[0.9992256,0.0000282846,0.0004409761,0.00006447163,0.0001527394,0.00008795952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009851005,0.0002089031,0.06932995,0.0001472641,0.0008805199,0.0000202594,0.001428252,0.4017654,0.4855525,0.0004183883,0.006767537,0.03249586],"study_design_scores_gemma":[0.01688952,0.009069947,0.07235552,0.0008000563,0.0004181207,0.004525914,0.005593046,0.06842452,0.6939567,0.01259127,0.111162,0.004213407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9045839,0.001174227,0.09396716,0.00006151083,0.0000839847,0.0000541952,0.00002395085,0.000004961817,0.00004608642],"genre_scores_gemma":[0.8633828,0.0003013563,0.1356497,0.0001100525,0.0002039424,0.000001312402,0.0002243132,0.000008130109,0.0001184408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3333409,"threshold_uncertainty_score":0.4400011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041361670249012,"score_gpt":0.2526595291995905,"score_spread":0.2422459124971004,"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."}}