{"id":"W2082232137","doi":"10.1142/s0219720004000776","title":"DISCOVERY OF FUNCTIONAL GENES FOR SYSTEMIC ACQUIRED RESISTANCE IN<i>ARABIDOPSIS THALIANA</i>THROUGH INTEGRATED DATA MINING","year":2004,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Plant Biotechnology Institute","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Gene; Cluster analysis; Arabidopsis thaliana; Computational biology; Arabidopsis; Biology; Genetics; Candidate gene; Promoter; Data mining; Computer science; Artificial intelligence; Mutant; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002572187,0.0000864692,0.0001830705,0.00008565802,0.00003641103,0.00001670061,0.0001723695,0.00008931012,0.000001037343],"category_scores_gemma":[0.0000673428,0.00006844987,0.00004832406,0.00009787626,0.00007931882,0.00003766659,0.00004958754,0.00003437397,1.799498e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001908671,"about_ca_system_score_gemma":0.0002649906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003074434,"about_ca_topic_score_gemma":0.0000117543,"domain_scores_codex":[0.9990954,0.0000256865,0.0005868712,0.0001108225,0.00008955173,0.00009171083],"domain_scores_gemma":[0.9990503,0.00004271323,0.0005124284,0.0001319287,0.000238784,0.00002382154],"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":[0.004699189,0.0006146945,0.01313244,0.001297929,0.0009121766,0.000006977176,0.002390344,0.06730875,0.8386256,0.03141135,0.01462414,0.02497637],"study_design_scores_gemma":[0.07441112,0.01155279,0.1100544,0.006765604,0.0009340654,0.003960544,0.04149223,0.1002054,0.2585609,0.1751628,0.2120523,0.004847829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5612636,0.003155365,0.4346505,0.0004536774,0.0001913824,0.0001145639,0.0001159134,0.000001700279,0.00005324942],"genre_scores_gemma":[0.9468089,0.0004543496,0.05196489,0.0001595036,0.00008573271,0.000004474005,0.0004882803,0.000005117916,0.00002873187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5800647,"threshold_uncertainty_score":0.2791305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.034269504117523,"score_gpt":0.2854363661434654,"score_spread":0.2511668620259424,"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."}}