{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000706264,0.00009184467,0.0001046528,0.00002932011,0.00005156685,0.00001269851,0.00009848506,0.0001116165,0.00003182246],"category_scores_gemma":[0.00001945374,0.00008554111,0.00004767053,0.00004135824,0.000007168181,0.00000461531,0.00002796573,0.00004381216,0.000003304528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002142755,"about_ca_system_score_gemma":0.00004641303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004407744,"about_ca_topic_score_gemma":0.000001078058,"domain_scores_codex":[0.9992018,0.00002750277,0.0001391958,0.0003192322,0.0001762125,0.0001360903],"domain_scores_gemma":[0.9994933,0.000003992281,0.0000574644,0.0002563064,0.0001418394,0.00004707697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001111965,0.0004882181,0.01624863,0.00007613709,0.00002574398,6.461274e-9,0.00001431493,0.002502084,0.979973,0.000005648716,0.0002039027,0.0003510962],"study_design_scores_gemma":[0.001086973,0.0001498378,0.001267971,0.00004516261,0.00002814324,1.930298e-8,0.0000216512,0.08733129,0.9092364,0.00001308412,0.0007009129,0.0001185235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653223,0.0001635453,0.03319333,0.00005916946,0.0001614486,0.0005215139,0.00005550543,0.00001832102,0.0005048697],"genre_scores_gemma":[0.9924083,0.00006039316,0.005247409,0.0001023754,0.0002596668,0.0003377663,0.0006968571,0.00002016995,0.0008670122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0848292,"threshold_uncertainty_score":0.3488265,"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."}}