{"id":"W3128741322","doi":"10.1109/bibm49941.2020.9312982","title":"Predictive Analytics on Genomic Data with High-Performance Computing","year":2020,"lang":"en","type":"article","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Association of Commonwealth Universities","keywords":"Genomics; Computer science; Genome; Computational biology; Computational genomics; Personal genomics; Human genome; Big data; Scalability; Gene; Data mining; Data science; Biology; Genetics; Database","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":[0.00204263,0.001292638,0.001179867,0.002174291,0.0009256785,0.002215215,0.001432402,0.0007201322,0.001141207],"category_scores_gemma":[0.008720341,0.0005145508,0.0009233016,0.003415755,0.0006561133,0.002615502,0.001390752,0.001397574,0.0007585222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009100995,"about_ca_system_score_gemma":0.001733471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01195086,"about_ca_topic_score_gemma":0.01025276,"domain_scores_codex":[0.9983301,0.0003816798,0.0001096883,0.0003888702,0.0006494556,0.000140247],"domain_scores_gemma":[0.9963548,0.001965958,0.0002428501,0.0006486159,0.0006156722,0.0001720388],"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":[0.0005580545,0.0004178212,0.01695896,0.000334848,0.0003759894,0.0007402432,0.0001832702,0.588688,0.006130552,0.0103149,0.01380351,0.3614938],"study_design_scores_gemma":[0.00001882849,0.00002774297,0.001050975,0.0000131129,0.00001552702,0.00006105087,0.00005366245,0.9781895,0.001368979,0.01803812,0.001152237,0.00001022119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1094495,0.002798619,0.8616747,0.002690644,0.0003362331,0.0003238998,0.002668714,0.01580528,0.004252353],"genre_scores_gemma":[0.7070902,0.001141209,0.285269,0.0003434844,0.0002095196,0.0001872549,0.004251323,0.0002449203,0.001263051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195086,"threshold_uncertainty_score":0.02376258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02319316318235679,"score_gpt":0.2282959523031881,"score_spread":0.2051027891208313,"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."}}