{"id":"W2118800699","doi":"10.1186/gb-2005-6-13-r115","title":"iVici: Interrelational Visualization and Correlation Interface","year":2005,"lang":"en","type":"article","venue":"Genome biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Genome Canada","keywords":"Visualization; Computer science; Overlay; Correlation; Set (abstract data type); Interface (matter); Data mining; Information visualization; Computational biology; Theoretical computer science; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003194186,0.001858696,0.00129945,0.002799785,0.0005725373,0.003064811,0.00257379,0.001044639,0.03186492],"category_scores_gemma":[0.009529437,0.000929477,0.001353509,0.002017378,0.0006004417,0.002688284,0.004439534,0.002373709,0.00863347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006611462,"about_ca_system_score_gemma":0.001090734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234814,"about_ca_topic_score_gemma":0.001577667,"domain_scores_codex":[0.9986656,0.0003488804,0.0001338872,0.0002197266,0.0004931838,0.0001386838],"domain_scores_gemma":[0.9967379,0.001653267,0.0001419602,0.0006852108,0.0005080742,0.0002735155],"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":[0.002225422,0.0003312971,0.005266374,0.001468276,0.0004193211,0.001400857,0.002033515,0.02044251,0.05973386,0.1050848,0.3587399,0.442854],"study_design_scores_gemma":[0.0004767923,0.0001608504,0.003773126,0.0003732708,0.0001530355,0.001022277,0.0002647432,0.4360071,0.05978141,0.0898368,0.4078983,0.0002524254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002551479,0.0001560992,0.8323838,0.0002945334,0.00009982784,0.0001522569,0.003160604,0.155841,0.005360424],"genre_scores_gemma":[0.07571997,0.0007673939,0.8692707,0.0007256011,0.0001624578,0.001605714,0.01792928,0.02462287,0.009195847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03186492,"threshold_uncertainty_score":0.1065987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006808359511202564,"score_gpt":0.2543127080351668,"score_spread":0.2475043485239642,"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."}}