{"id":"W2084557687","doi":"10.2165/00822942-200504010-00001","title":"Spot Detection and Image Segmentation in DNA??Microarray Data","year":2005,"lang":"en","type":"review","venue":"Applied Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; IBM (Canada)","funders":"","keywords":"DNA microarray; Cluster analysis; Segmentation; Histogram; Computer science; Data mining; Gene chip analysis; Euclidean distance; Artificial intelligence; Microarray analysis techniques; Pattern recognition (psychology); Image (mathematics); Biology; Gene; Genetics","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":[0.0002253105,0.0002475383,0.0003496494,0.0001373055,0.00004909508,0.00006145974,0.000309448,0.0003135452,0.000009429062],"category_scores_gemma":[0.00001496232,0.0002160321,0.00004590238,0.0001614939,0.00004688281,0.00001420805,0.0002410172,0.0001507554,0.00003816542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004098924,"about_ca_system_score_gemma":0.00009605577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001813369,"about_ca_topic_score_gemma":0.00002028711,"domain_scores_codex":[0.9988361,0.00002461057,0.0005409014,0.0003142029,0.0001133617,0.0001708162],"domain_scores_gemma":[0.9988947,0.000008797369,0.0003273365,0.0006964938,0.00001803223,0.00005462866],"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":[0.000006523267,0.0000149378,3.217245e-7,0.001248846,0.00001702259,1.235744e-7,0.00003466721,4.586225e-7,0.01073103,0.000006955264,0.0008154599,0.9871237],"study_design_scores_gemma":[0.0002673926,0.0000198554,0.000004428887,0.0002688847,0.00007750736,0.00001350925,0.0001167372,0.0001082783,0.003677142,0.000005124097,0.9951825,0.0002586808],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001225507,0.9887251,0.006174134,0.0000162447,0.0001530603,0.001071395,0.0001265896,0.00002378045,0.003587109],"genre_scores_gemma":[0.0002193129,0.9900764,0.006438895,0.00007239759,0.0001671659,0.00009238524,0.002815003,0.00002642626,0.00009204326],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.994367,"threshold_uncertainty_score":0.8809534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03628206879582436,"score_gpt":0.3202165621431473,"score_spread":0.2839344933473229,"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."}}