{"id":"W2344996561","doi":"10.1186/s13104-016-2023-5","title":"Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends","year":2016,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Breast cancer; Data science; Disease; Computer science; Computational biology; Cancer; Bioinformatics; Data mining; Gene; Medicine; Biology; Genetics; Pathology","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":[0.001328216,0.0001555345,0.0002467511,0.0002003632,0.0002618672,0.00009870757,0.0005838199,0.0001817944,0.00008027281],"category_scores_gemma":[0.001855513,0.0001016118,0.00008787998,0.0005346123,0.0004393216,0.000008275369,0.0006886804,0.00007841892,0.000001629317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001709343,"about_ca_system_score_gemma":0.0001281631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001978308,"about_ca_topic_score_gemma":0.003521264,"domain_scores_codex":[0.9979165,0.000197397,0.0002539504,0.0007284316,0.0002820668,0.0006216149],"domain_scores_gemma":[0.998135,0.0007626078,0.0000805468,0.0006933764,0.0001715021,0.0001569844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001040601,0.00002107184,0.3997338,0.00003651252,0.0003266007,0.000001894447,0.0000781167,0.000008587803,0.01748388,0.000008710721,0.01558628,0.5666105],"study_design_scores_gemma":[0.002064261,0.0006787801,0.9200354,0.0004525428,0.0004485437,0.00003680194,0.001145295,0.007823746,0.005736845,0.0002048633,0.06057308,0.0007998886],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9102141,0.008560249,0.07865971,0.001505462,0.000168714,0.0001822823,0.0005137331,0.0000361859,0.00015959],"genre_scores_gemma":[0.9306037,0.0006965155,0.06584992,0.00004623806,0.0009127317,0.00009383861,0.0001460348,0.00002618812,0.001624814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5658106,"threshold_uncertainty_score":0.4143609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2226281018214584,"score_gpt":0.4563182806821742,"score_spread":0.2336901788607158,"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."}}