{"id":"W2125353579","doi":"10.1093/bioinformatics/bth470","title":"SNP Chart: an integrated platform for visualization and interpretation of microarray genotyping data","year":2004,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"Health Canada; Michael Smith Health Research BC","keywords":"SNP; Computer science; Replicate; Visualization; Chart; Genotyping; SNP genotyping; Data mining; SNP array; Single-nucleotide polymorphism; Data visualization; Computational biology; Biology; Genotype; Genetics; Statistics; 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":[0.0001382767,0.00007415892,0.00007593894,0.00004842291,0.00004435517,0.00002272009,0.000145086,0.00008125554,0.000002032727],"category_scores_gemma":[0.00005924996,0.00006619037,0.0000156745,0.00005969956,0.00003297284,0.0000326659,0.00005296955,0.00002167508,9.055427e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001113686,"about_ca_system_score_gemma":0.00007291107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004550605,"about_ca_topic_score_gemma":0.00001388589,"domain_scores_codex":[0.9994946,0.000006093635,0.0002439102,0.0001184046,0.00005710668,0.00007985525],"domain_scores_gemma":[0.9994084,0.00000361804,0.0001510366,0.0003021835,0.00009756,0.00003724049],"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":[0.0002658239,0.00008120182,0.0001328122,0.0002870567,0.00004392931,4.189872e-8,0.002938736,0.0003699281,0.8792762,0.002123428,0.00100831,0.1134725],"study_design_scores_gemma":[0.001991034,0.0006301847,0.000611766,0.0001429761,0.0000458685,0.000008836701,0.003339222,0.209512,0.7449278,0.0004065353,0.03805479,0.0003289706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1812146,0.000146041,0.8180313,0.00003111366,0.00009095653,0.0002486624,0.00009467168,0.00001091613,0.000131695],"genre_scores_gemma":[0.9649923,0.0001416939,0.03107131,0.0001441856,0.00004408176,0.00001250405,0.003553142,0.00001048535,0.00003032265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.78696,"threshold_uncertainty_score":0.2699165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03967467682655473,"score_gpt":0.3162916687121121,"score_spread":0.2766169918855574,"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."}}