{"id":"W2163649997","doi":"10.1093/bioinformatics/btl080","title":"MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"Heart and Stroke Foundation of British Columbia and Yukon; National Sanitarium Association; Heart and Stroke Foundation of Canada","keywords":"Genotyping; Cluster analysis; SNP; SNP genotyping; Computer science; DNA microarray; Single-nucleotide polymorphism; Computational biology; Genotype; SNP array; Data mining; Biology; Artificial intelligence; 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":[0.0002219923,0.0001199878,0.0001738591,0.000159564,0.0000741553,0.00002339931,0.0002684885,0.0001108792,0.000008461213],"category_scores_gemma":[0.00005207255,0.0001134064,0.0001006844,0.0002419018,0.00003653183,0.00001062738,0.0001090749,0.00002975966,0.000001884858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002779982,"about_ca_system_score_gemma":0.0001507785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002258586,"about_ca_topic_score_gemma":0.00004862924,"domain_scores_codex":[0.999025,0.0000157136,0.0004779561,0.0001922173,0.0001208361,0.0001682227],"domain_scores_gemma":[0.998924,0.00001685471,0.0002751311,0.0006268416,0.0001265435,0.00003068528],"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":[0.00005760072,0.00003879381,0.0008022212,0.0001255885,0.0001670852,1.477503e-7,0.00003816267,0.04083039,0.9522318,0.00001038368,0.004386133,0.001311745],"study_design_scores_gemma":[0.0005376775,0.00002694068,0.002313673,0.00002015254,0.0001668903,8.262426e-7,0.00004748212,0.853156,0.1394325,0.000001301437,0.004159648,0.0001368105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3640381,0.0002328541,0.6342893,0.00006000744,0.0001659383,0.0003866684,0.0006169101,0.0000663988,0.0001437819],"genre_scores_gemma":[0.723919,0.00000639551,0.2698455,0.0002068801,0.00005807048,0.00001944467,0.005812685,0.00001477886,0.0001172739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8127992,"threshold_uncertainty_score":0.4624579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04516803501496724,"score_gpt":0.3026467413629859,"score_spread":0.2574787063480187,"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."}}