{"id":"W1979217166","doi":"10.1186/1471-2105-13-54","title":"Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm","year":2012,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Plant Biotechnology Institute; National Research Council Canada","funders":"National Research Council Canada","keywords":"Cluster analysis; Biological data; Data mining; Computer science; Arabidopsis thaliana; Data set; DNA microarray; Algorithm; Computational biology; Biology; Artificial intelligence; Pattern recognition (psychology); Bioinformatics; Gene; Gene expression; Genetics","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":[0.0003612104,0.0001683501,0.0001265598,0.00004214394,0.0001780571,0.00008766462,0.0005090925,0.0002089336,0.00005628235],"category_scores_gemma":[0.0001070111,0.0001051986,0.00004722079,0.0001073252,0.00007730268,0.0001602835,0.0005229164,0.00008817281,0.0001291203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001477421,"about_ca_system_score_gemma":0.00005576627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002792779,"about_ca_topic_score_gemma":0.00000105914,"domain_scores_codex":[0.9989035,0.00006051937,0.0004234725,0.0001344801,0.0002095874,0.0002685083],"domain_scores_gemma":[0.9987837,0.00002687002,0.0001661788,0.0008580245,0.00006527486,0.00009991566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004428196,0.0002351792,0.01916215,0.00008815549,0.0001502007,3.743377e-7,0.003558691,0.0003526816,0.24049,0.00004443402,0.258439,0.4770362],"study_design_scores_gemma":[0.0007454071,0.0001285108,0.008085449,0.00003515121,0.00004748116,0.00002500937,0.00190755,0.08253219,0.1898967,0.0000177315,0.7160429,0.0005359029],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04246374,0.0009043514,0.9516168,0.0001190219,0.0007259278,0.0004753143,0.0003421097,0.00006223075,0.003290521],"genre_scores_gemma":[0.04779647,0.0004968118,0.9380053,0.001165155,0.001243977,0.0000807043,0.01081311,0.00002292625,0.0003756114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4765003,"threshold_uncertainty_score":0.4289874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04298185814857665,"score_gpt":0.2708044035176717,"score_spread":0.227822545369095,"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."}}