{"id":"W1964215490","doi":"10.1142/9789812704856_0054","title":"MODELING GENE EXPRESSION FROM MICROARRAY EXPRESSION DATA WITH STATE-SPACE EQUATIONS","year":2003,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Expression (computer science); State variable; State space; State (computer science); Computer science; Gene expression; Bayesian information criterion; State-space representation; Gene; Mathematics; Algorithm; Biology; Artificial intelligence; Statistics; Genetics; Physics","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":[0.0002067167,0.0001784085,0.000113448,0.00005140893,0.0001483604,0.00004641948,0.0003595063,0.0001272464,0.0001285315],"category_scores_gemma":[0.00007861997,0.0001347182,0.00002845174,0.0001077177,0.00003243305,0.00001692569,0.0001468029,0.00009227397,0.00001552225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001406685,"about_ca_system_score_gemma":0.0001227104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004396743,"about_ca_topic_score_gemma":0.00005820849,"domain_scores_codex":[0.9985405,0.00008977011,0.0002192375,0.0006983845,0.0002190484,0.0002330361],"domain_scores_gemma":[0.9983377,0.00001559346,0.00008063568,0.001330637,0.0001039586,0.0001314312],"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":[0.00008578998,0.00005369466,0.0002070026,0.000002822787,0.00001138657,6.821929e-7,0.00006010984,0.004209381,0.991946,0.00003259284,0.002994575,0.000396009],"study_design_scores_gemma":[0.0005711033,0.00004332852,0.00002618471,0.00003000727,0.0000100637,0.000001622165,0.0003532688,0.00488697,0.9772139,0.00008416271,0.01655899,0.0002204533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2739378,0.0005418456,0.7238846,0.000067306,0.0000920538,0.0001399283,0.00004676677,0.000024069,0.001265561],"genre_scores_gemma":[0.9294342,0.0001414723,0.06741564,0.0001691361,0.00008759761,0.0000230075,0.001211492,0.00003181632,0.001485591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.656469,"threshold_uncertainty_score":0.5493649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03654210658871324,"score_gpt":0.2736676641808078,"score_spread":0.2371255575920946,"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."}}