{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002911679,0.001434883,0.0007043775,0.004405376,0.0005509187,0.002934991,0.001289667,0.0009999151,0.01457434],"category_scores_gemma":[0.01402037,0.0005736,0.001574055,0.002277491,0.0004467159,0.003016863,0.002815127,0.001624728,0.00232258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007815884,"about_ca_system_score_gemma":0.001033876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004077164,"about_ca_topic_score_gemma":0.005721174,"domain_scores_codex":[0.9991704,0.0003440196,0.00009136325,0.0001635733,0.0001798184,0.00005075688],"domain_scores_gemma":[0.993534,0.004152842,0.0005311206,0.0008104916,0.0007401205,0.0002315373],"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":[0.001877755,0.0003603319,0.01792,0.001976628,0.0004216997,0.0009318514,0.007165637,0.04834547,0.02247701,0.0683714,0.1342398,0.6959124],"study_design_scores_gemma":[0.0003841938,0.0003025534,0.01062667,0.0005184573,0.0002077136,0.0007285073,0.001514849,0.6478126,0.02273578,0.1455637,0.1693986,0.0002063347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01369617,0.0004357797,0.9230673,0.0008250531,0.0001409052,0.0002752888,0.009947478,0.04870741,0.002904546],"genre_scores_gemma":[0.1693258,0.000623988,0.8097365,0.0002769564,0.0001048063,0.0009893683,0.01079082,0.005822621,0.002329234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01457434,"threshold_uncertainty_score":0.04875606,"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."}}