{"id":"W2984507973","doi":"10.1371/journal.pone.0224446","title":"Gene expression based survival prediction for cancer patients—A topic modeling approach","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Broad Institute; Compute Canada","keywords":"Latent Dirichlet allocation; Computer science; Breast cancer; Cancer; Artificial intelligence; Topic model; Inference; Expression (computer science); Machine learning; Medicine","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":[0.003768676,0.0009767471,0.001317733,0.002007168,0.0004837425,0.0008674333,0.001455393,0.001198456,0.00127448],"category_scores_gemma":[0.005542753,0.0003481474,0.00226781,0.001713209,0.0004223226,0.001080841,0.0007975009,0.001783623,0.0006497855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035759,"about_ca_system_score_gemma":0.0009730944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00962244,"about_ca_topic_score_gemma":0.01004631,"domain_scores_codex":[0.9987701,0.0005245612,0.00007874201,0.000355928,0.0001488813,0.0001218471],"domain_scores_gemma":[0.9974935,0.00192677,0.0001038872,0.0001547585,0.0002485965,0.00007240582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001083693,0.0004448151,0.03943054,0.0002598407,0.0004717669,0.0002590728,0.0008477168,0.6310921,0.005954086,0.01022562,0.01491632,0.2950146],"study_design_scores_gemma":[0.00003084001,0.00004149677,0.00170957,0.00001113557,0.00003471903,0.00005259594,0.00004746778,0.9885168,0.0007986702,0.00728514,0.001454878,0.00001664819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1845994,0.003546989,0.7965967,0.003286289,0.0002124766,0.0003633462,0.006557238,0.003108553,0.001728976],"genre_scores_gemma":[0.744172,0.001399011,0.2361268,0.000582816,0.0004610445,0.0006177094,0.01297089,0.0001970315,0.003472513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00962244,"threshold_uncertainty_score":0.01993096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04522873409577578,"score_gpt":0.2406245578639247,"score_spread":0.1953958237681489,"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."}}