{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004505812,0.0003476958,0.0005958325,0.001723738,0.0002216544,0.0004397023,0.0003538381,0.0002667763,0.0004650282],"category_scores_gemma":[0.0006828672,0.0001209671,0.0006783291,0.001461236,0.0001829692,0.0002300152,0.0002022451,0.0003267926,0.0002120624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003658466,"about_ca_system_score_gemma":0.0005253697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001057953,"about_ca_topic_score_gemma":0.001373018,"domain_scores_codex":[0.9997995,0.00002724222,0.00001871483,0.00006964657,0.0000583299,0.00002662598],"domain_scores_gemma":[0.999615,0.0001400987,0.00007791982,0.00002936245,0.00009467909,0.00004284309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001186373,0.0004822256,0.141237,0.0007552325,0.0002457601,0.0009000375,0.0002564879,0.006712774,0.6807703,0.001227839,0.001222839,0.1650031],"study_design_scores_gemma":[0.0001446355,0.001091714,0.5175424,0.0001096221,0.0006953346,0.002249454,0.0006565386,0.1552316,0.3097619,0.003927732,0.008485974,0.0001031484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.943792,0.000769163,0.04931856,0.0001993276,0.000009599127,0.00009269355,0.004387286,0.0008058969,0.0006254737],"genre_scores_gemma":[0.8780556,0.0003008988,0.1112049,0.00005676737,0.000009441363,0.0001304333,0.009803416,0.00004284331,0.0003957159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001723738,"threshold_uncertainty_score":0.002654493,"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."}}