{"id":"W4312074752","doi":"10.1101/2022.12.06.519415","title":"Baseline Acute Myeloid Leukemia Prognosis Models using Transcriptomic and Clinical Profiles by Studying the Impacts of Dimensionality Reductions and Gene Signatures on Cox-Proportional Hazard","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont; Institute for Research in Immunology and Cancer","funders":"","keywords":"Overfitting; Myeloid leukemia; Context (archaeology); Proportional hazards model; Computer science; Artificial intelligence; Machine learning; Hazard ratio; Dimensionality reduction; Feature selection; Data mining; Biology; Statistics; Mathematics; Cancer research","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001494225,0.0005980605,0.0004076238,0.0005026169,0.0002242318,0.0007681963,0.0004655988,0.0003761345,0.001201587],"category_scores_gemma":[0.002816719,0.0001617416,0.0007888716,0.0004905283,0.0002111662,0.0004077781,0.0004718416,0.0008935921,0.0003062034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006198803,"about_ca_system_score_gemma":0.0008887816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01055277,"about_ca_topic_score_gemma":0.006861955,"domain_scores_codex":[0.9996597,0.0001517799,0.00001766454,0.00008312909,0.00004390549,0.0000439354],"domain_scores_gemma":[0.9989607,0.0006476567,0.0001071479,0.0001126084,0.0001077007,0.0000642058],"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":[0.0006444825,0.0002276474,0.09532666,0.00004457198,0.0002347503,0.0001095613,0.00006202421,0.8566178,0.003175479,0.001159338,0.001510479,0.04088724],"study_design_scores_gemma":[0.00000651803,0.00005816975,0.008291379,0.000003729002,0.00001399115,0.00001837639,0.00001924503,0.9901088,0.0006058716,0.0007232804,0.0001432439,0.000007339896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9395323,0.0002804997,0.05653947,0.0006745093,0.00002645862,0.00005549508,0.001810555,0.0003409557,0.0007397319],"genre_scores_gemma":[0.9901118,0.00006757849,0.007363651,0.00003253016,0.00001143513,0.00003614202,0.001803083,0.00001130406,0.0005624956],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01055277,"threshold_uncertainty_score":0.02098268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04464699534108364,"score_gpt":0.3147631318240592,"score_spread":0.2701161364829756,"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."}}