{"id":"W2752320387","doi":"10.1109/tvcg.2017.2745118","title":"PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Ontario Genomics; Genome Canada","keywords":"Computer science; Relevance (law); Subtyping; Machine learning; Workflow; Phenotype; Artificial intelligence; Data visualization; Data mining; Data science; Visualization; Biology","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":[0.00008697849,0.0001527565,0.0001521224,0.00008126555,0.0007524933,0.0001163044,0.0001743769,0.0001374001,0.000003211864],"category_scores_gemma":[0.00001015641,0.0001493426,0.00008737714,0.00006369349,0.0001702121,0.0000141303,0.000006187262,0.00006485129,8.744466e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005496734,"about_ca_system_score_gemma":0.00002847137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001054456,"about_ca_topic_score_gemma":0.0000370275,"domain_scores_codex":[0.9991531,0.00003713278,0.0002133751,0.0003226094,0.00010779,0.0001660149],"domain_scores_gemma":[0.999283,0.00002597154,0.0001080784,0.00033284,0.0001257186,0.0001244338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002102357,0.004290193,0.006602495,0.001161119,0.001254018,0.000008630611,0.003415331,0.03034642,0.003379428,0.4296288,0.007875781,0.5099354],"study_design_scores_gemma":[0.0007796796,0.0002869418,0.0006812523,0.00004039374,0.00005999937,0.000001423683,0.00002850153,0.9889064,0.00161341,0.001704081,0.005660717,0.0002372094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02676219,0.0001721506,0.9721313,0.0001329276,0.0004971567,0.0002055485,0.00002961341,0.00005243241,0.00001671933],"genre_scores_gemma":[0.9973646,0.0002581468,0.001226059,0.0007466892,0.0001798273,0.00004501759,0.0000762901,0.00002127353,0.00008211182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9709052,"threshold_uncertainty_score":0.6090016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04814796640478376,"score_gpt":0.3388978814107913,"score_spread":0.2907499150060076,"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."}}