{"id":"W2142963728","doi":"10.1093/bioinformatics/btt602","title":"Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University Health Network","funders":"Government of Ontario; Eli Lilly Canada; University Health Network; Natural Sciences and Engineering Research Council of Canada; Ottawa Hospital Research Institute; Ontario Institute for Cancer Research; Eli Lilly and Company","keywords":"Computer science; Cluster analysis; Representation (politics); Set (abstract data type); Univariate; Dynamical systems theory; Visualization; Algorithm; Artificial intelligence; External Data Representation; Pattern recognition (psychology); Data mining; Machine learning; Multivariate statistics","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":[0.0001142631,0.0001445398,0.0001285714,0.00006352768,0.0001502081,0.0003427369,0.000894906,0.00003584333,0.000003284105],"category_scores_gemma":[0.000006892006,0.00009971956,0.00001845762,0.0002794181,0.00003948871,0.001582247,0.0002287697,0.0001277943,0.0001386509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003781445,"about_ca_system_score_gemma":0.00004243933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005383507,"about_ca_topic_score_gemma":0.000001256443,"domain_scores_codex":[0.998835,0.00001227727,0.0003055311,0.0002385717,0.0003786343,0.0002300468],"domain_scores_gemma":[0.9983134,0.00009709791,0.000147674,0.001252091,0.0000749985,0.0001147138],"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":[0.00003020405,0.0008333066,0.0003694001,0.0002926992,0.0001148156,0.000007587579,0.0004419544,0.1352453,0.001049281,0.3157425,0.01467729,0.5311956],"study_design_scores_gemma":[0.0002407365,0.00006231401,0.001978344,0.00006157406,0.000005234124,0.000007350255,0.00002779193,0.9966363,0.00002026958,0.00004270864,0.0007621841,0.0001552425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005074134,0.000003261535,0.9923884,0.001590314,0.0001897378,0.0004022488,0.0000582239,0.000138056,0.0001556174],"genre_scores_gemma":[0.5248898,0.000003266848,0.4741288,0.0004016942,0.00008901251,0.0001798721,0.0002676105,0.00001061424,0.00002930699],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8613909,"threshold_uncertainty_score":0.4066446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09020328266180674,"score_gpt":0.2489412473830473,"score_spread":0.1587379647212405,"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."}}