{"id":"W3180707480","doi":"10.1007/978-3-030-76732-7_5","title":"Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction","year":2021,"lang":"en","type":"book-chapter","venue":"Intelligent systems reference library","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Big data; Genome; Gene; Genomics; Machine learning; Confusion matrix; Computational genomics; Artificial intelligence; Coding (social sciences); Gene prediction; Computational biology; Data mining; Biology; Genetics; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002253318,0.0003915589,0.0005450968,0.0001460267,0.00007723346,0.00007656887,0.0005531643,0.0005700205,0.00004202651],"category_scores_gemma":[0.00006721717,0.0003744149,0.000102902,0.00003422506,0.0001060577,0.0000211578,0.0009411449,0.0004308686,0.000006629572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000178225,"about_ca_system_score_gemma":0.0002078895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002446294,"about_ca_topic_score_gemma":0.000003267434,"domain_scores_codex":[0.9980689,0.0000617269,0.0008220355,0.0006119727,0.0002172979,0.0002180432],"domain_scores_gemma":[0.9980462,0.00006480503,0.0006914603,0.0009649635,0.0001237754,0.000108783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002209464,0.0005286227,0.009755462,0.01713667,0.01646359,0.0001297134,0.001287222,0.02480198,0.03185224,0.08678199,0.0531947,0.7558584],"study_design_scores_gemma":[0.0002749868,0.0007386899,0.00003194896,0.0005617113,0.0002193131,0.00008692629,0.0001080093,0.1789758,0.003043952,0.0001899236,0.8152405,0.0005282647],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001483943,0.0960144,0.6781615,0.0001311504,0.002919439,0.004049852,0.04204422,0.0002587585,0.1749367],"genre_scores_gemma":[0.02507637,0.04322812,0.06162535,0.0001529033,0.002853198,0.00007733845,0.1453334,0.0004948727,0.7211585],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7620458,"threshold_uncertainty_score":0.9998708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05479591821128172,"score_gpt":0.2542025929547279,"score_spread":0.1994066747434462,"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."}}