{"id":"W2073661114","doi":"10.1109/iembs.2010.5626506","title":"Identification of gene regulatory networks from time course gene expression data","year":2010,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gene regulatory network; Inference; Computer science; Identification (biology); Computational biology; Gene; Constraint (computer-aided design); Gene expression; Regulation of gene expression; Systems biology; Regulator gene; Expression (computer science); Data mining; Biology; Artificial intelligence; Genetics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001449174,0.0009275047,0.000879098,0.002523949,0.0004078166,0.0005593139,0.001007687,0.0007344784,0.000734456],"category_scores_gemma":[0.005346663,0.0004688862,0.001265332,0.002042674,0.0005844947,0.000948766,0.0004525599,0.001198628,0.0002144377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007631773,"about_ca_system_score_gemma":0.0008493803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005404224,"about_ca_topic_score_gemma":0.008799019,"domain_scores_codex":[0.999238,0.0002212722,0.00003442245,0.0003061259,0.000146837,0.00005333259],"domain_scores_gemma":[0.9964997,0.002610617,0.0004337174,0.0002036002,0.0001943938,0.00005811271],"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":[0.0004223857,0.0003047173,0.02288842,0.0006560722,0.0004604815,0.0003098291,0.0002294172,0.7182469,0.04689542,0.0109684,0.00174504,0.1968729],"study_design_scores_gemma":[0.00001468821,0.00002668948,0.005103149,0.00000938844,0.00003861636,0.00005954299,0.00002342887,0.9789532,0.004991015,0.009841896,0.0009211936,0.00001712759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09499909,0.0008653103,0.9003706,0.0002254156,0.00001374297,0.0001305487,0.002069003,0.0008055539,0.0005206238],"genre_scores_gemma":[0.4039035,0.001184262,0.5834722,0.0001032644,0.00004083657,0.0003968382,0.009991684,0.0000879466,0.0008195278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005404224,"threshold_uncertainty_score":0.01074558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007909618373417412,"score_gpt":0.2387879705795496,"score_spread":0.2308783522061322,"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."}}