{"id":"W2114442655","doi":"10.1007/s00500-014-1242-8","title":"A hierarchical nonparametric Bayesian approach for medical images and gene expressions classification","year":2014,"lang":"en","type":"article","venue":"Soft Computing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Interpretability; Mixture model; Artificial intelligence; Machine learning; Model selection; Markov chain Monte Carlo; Feature selection; Outlier; Feature (linguistics); Dirichlet process; Data mining; Generative model; Gaussian process; Bayesian probability; Gaussian; Generative grammar","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001719701,0.0001536972,0.0002391265,0.0001589127,0.0003170871,0.0001665402,0.0006146083,0.0001477748,0.000001758922],"category_scores_gemma":[0.0008955529,0.0001292828,0.00007137167,0.0003676351,0.0000922062,0.0001364331,0.0003237479,0.0002586138,0.000001136469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001460031,"about_ca_system_score_gemma":0.00006190765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003326358,"about_ca_topic_score_gemma":1.455291e-7,"domain_scores_codex":[0.9981734,0.0002653321,0.0002965741,0.0005955532,0.0003169685,0.0003521899],"domain_scores_gemma":[0.998028,0.001108743,0.0001039792,0.00041788,0.0000667983,0.0002745695],"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":[0.000003972445,0.00006627596,0.0001805567,0.00004051604,0.000009023041,0.000001187752,0.0002729538,0.00003926662,0.001543751,0.1019797,0.0003583798,0.8955044],"study_design_scores_gemma":[0.0003639988,0.00004598956,0.00118548,0.00002463693,0.000006334187,0.00004062338,0.000005392805,0.9723127,0.000459173,0.02495266,0.0004394058,0.0001635823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001301727,0.0001405815,0.9957442,0.0009861906,0.000149745,0.0002323587,0.000001291576,0.0001727532,0.001271175],"genre_scores_gemma":[0.4030004,0.000003634641,0.5964795,0.0002884024,0.0001838788,0.00001156433,0.000003580519,0.000008815445,0.0000201862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9722735,"threshold_uncertainty_score":0.5272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183240981667998,"score_gpt":0.2929382043073898,"score_spread":0.2711057944907098,"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."}}