{"id":"W2783873704","doi":"10.1186/s12859-017-1996-y","title":"diceR: an R package for class discovery using an ensemble driven approach","year":2018,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"BC Cancer Foundation","keywords":"Computer science; Dicer; Cluster analysis; Context (archaeology); Data mining; Set (abstract data type); Class (philosophy); Permutation (music); Generalization; Cluster (spacecraft); Machine learning; Artificial intelligence; Data 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":[0.02315324,0.003914279,0.004704414,0.009180763,0.002275164,0.00632269,0.007729058,0.002847473,0.06165078],"category_scores_gemma":[0.1075756,0.003794423,0.005900973,0.006671592,0.002340676,0.004112719,0.00638744,0.007684025,0.03254007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686317,"about_ca_system_score_gemma":0.007401756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004472909,"about_ca_topic_score_gemma":0.00654537,"domain_scores_codex":[0.9837514,0.007529731,0.001727452,0.003400197,0.003158304,0.0004330793],"domain_scores_gemma":[0.9297916,0.04856842,0.004834617,0.008724692,0.007000038,0.001080582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001074285,0.0001467254,0.007428849,0.006614684,0.004286401,0.000597728,0.001129893,0.02779114,0.004328352,0.04227517,0.6374006,0.2669263],"study_design_scores_gemma":[0.0009468817,0.0002310705,0.005912778,0.001066447,0.001400438,0.001232554,0.0003029339,0.1583235,0.01158805,0.1955446,0.6228955,0.0005551748],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001370723,0.001063031,0.8547183,0.0006726391,0.0005547235,0.0008160069,0.03219429,0.1061607,0.002449653],"genre_scores_gemma":[0.01364631,0.0006818832,0.9248242,0.0007313746,0.0002745494,0.004755282,0.02133904,0.03176516,0.001982178],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06165078,"threshold_uncertainty_score":0.2062424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08579926171176273,"score_gpt":0.3410082939630839,"score_spread":0.2552090322513211,"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."}}