{"id":"W2161938868","doi":"10.1093/bioinformatics/btv192","title":"Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Hierarchy; Hierarchical clustering; Gene; Computational biology; Metric (unit); Tree (set theory); Euclidean distance; Cluster analysis; Biology; Computer science; Mathematics; Genetics; Artificial intelligence; Combinatorics","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":[0.0001681001,0.00007156777,0.00006816736,0.00003320543,0.00005978813,0.00001633378,0.00008449481,0.000084559,7.317754e-7],"category_scores_gemma":[0.0000122766,0.00005056781,0.00005237982,0.00009179896,0.0000261726,0.000008211523,0.00001871711,0.00003193789,9.968057e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001645746,"about_ca_system_score_gemma":0.00006928889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002726802,"about_ca_topic_score_gemma":0.000002707176,"domain_scores_codex":[0.9994777,0.0000165669,0.0002324685,0.00008254596,0.000100191,0.00009045709],"domain_scores_gemma":[0.9994259,0.000006412544,0.0002145753,0.0001544086,0.0001627787,0.00003585106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003108818,0.0001997715,0.002792396,0.000294657,0.00008874445,8.77541e-9,0.001708761,0.003247445,0.3632362,0.00013313,0.004648749,0.6233392],"study_design_scores_gemma":[0.001049569,0.0003496131,0.0004353195,0.00001151963,0.00005465167,0.000009694934,0.00310568,0.235493,0.735725,0.00002257393,0.02359514,0.0001482673],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2051784,0.0002603773,0.7930123,0.00003605079,0.0002691755,0.0006222394,0.00001148727,0.0000176485,0.0005923071],"genre_scores_gemma":[0.9432503,0.00006704831,0.05587963,0.00002381912,0.0001635258,0.0001136521,0.00014571,0.000009943551,0.0003463731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7380719,"threshold_uncertainty_score":0.2062096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283974096337588,"score_gpt":0.2501362259839564,"score_spread":0.2172964850205805,"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."}}