{"id":"W1996146483","doi":"10.1142/s0219720005001314","title":"DYNAMIC MODEL-BASED CLUSTERING FOR TIME–COURSE GENE EXPRESSION DATA","year":2005,"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":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Atomic Energy of Canada Limited; Natural Sciences and Engineering Research Council of Canada; Technische Universiteit Delft; University of Saskatchewan","keywords":"Cluster analysis; Computer science; Data mining; Bootstrapping (finance); Expression (computer science); Hierarchical clustering; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002586746,0.00008852001,0.0001248368,0.0000645824,0.00006913213,0.00002073622,0.000216878,0.00009202177,0.000004618199],"category_scores_gemma":[0.00003045955,0.00007094177,0.00004625006,0.00003086673,0.00004401294,0.00001788089,0.0000891903,0.00005214856,0.000001557668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001098292,"about_ca_system_score_gemma":0.0001620649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.978795e-8,"about_ca_topic_score_gemma":6.393782e-7,"domain_scores_codex":[0.9993083,0.00001793747,0.0003817946,0.0001092527,0.00007852483,0.0001042253],"domain_scores_gemma":[0.9992405,0.00002575451,0.0003311185,0.0001668624,0.0001710935,0.00006468586],"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":[0.0003244095,0.0001115872,0.0001087857,0.00004766103,0.00006389058,2.187426e-7,0.00007250984,0.5115602,0.4041039,0.00008313604,0.007742705,0.07578098],"study_design_scores_gemma":[0.000839658,0.0001999835,0.0001122436,0.00001723272,0.00001682629,0.00002475101,0.00002329751,0.984911,0.005605751,0.0003582248,0.007801624,0.00008941311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05722344,0.0005213713,0.9412156,0.0007270547,0.00007855304,0.00009153145,0.00008848548,0.000003269342,0.00005071945],"genre_scores_gemma":[0.6066876,0.00007843267,0.3920031,0.0004991284,0.0001092945,0.000003341627,0.0005660161,0.000007009536,0.00004606484],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5494642,"threshold_uncertainty_score":0.2892922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843663717349181,"score_gpt":0.3028210396366478,"score_spread":0.284384402463156,"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."}}