{"id":"W1549086459","doi":"10.1007/978-3-540-71080-6_21","title":"Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Visualization; Data set; Data mining; Data visualization; Set (abstract data type); Data science; Domain (mathematical analysis); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009754039,0.0004728529,0.0003585643,0.002959413,0.000218647,0.001422955,0.0009525656,0.0003931605,0.003549375],"category_scores_gemma":[0.003113698,0.000204473,0.0006941841,0.002746836,0.0002908879,0.0006810672,0.0007631937,0.0003682735,0.0007095253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003445358,"about_ca_system_score_gemma":0.0004320419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002763191,"about_ca_topic_score_gemma":0.005409326,"domain_scores_codex":[0.9996898,0.00007680655,0.00003117494,0.00006306088,0.0001142806,0.00002488028],"domain_scores_gemma":[0.9988865,0.0007285077,0.00008633015,0.00008615514,0.0001455355,0.00006703872],"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":[0.0002841603,0.0001185867,0.02314791,0.001081731,0.0002036438,0.001321051,0.0008966834,0.003135107,0.01299834,0.004931237,0.0423207,0.9095609],"study_design_scores_gemma":[0.0002213485,0.0007751771,0.2884477,0.002473654,0.001346578,0.02794377,0.00671957,0.1810834,0.0533013,0.1229989,0.3144093,0.0002793048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4052214,0.03293089,0.4633178,0.01016635,0.0009573707,0.000800637,0.02466126,0.008911405,0.0530328],"genre_scores_gemma":[0.5195456,0.01085689,0.4336124,0.0008514388,0.0004626721,0.0002566841,0.02002896,0.0006508192,0.01373453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003549375,"threshold_uncertainty_score":0.01187384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03650813956180147,"score_gpt":0.2948513237571672,"score_spread":0.2583431841953657,"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."}}