{"id":"W3000887590","doi":"10.1016/j.ygeno.2020.01.017","title":"Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features","year":2020,"lang":"en","type":"article","venue":"Genomics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Taipei Medical University; Nvidia","keywords":"Biology; Computational biology; Replication (statistics); DNA replication; Boosting (machine learning); Benchmark (surveying); Mechanism (biology); DNA sequencing; Computer science; Gene; Artificial intelligence; Genetics; Machine learning","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":[0.0009754354,0.0003782798,0.0006625736,0.0005799861,0.0002934954,0.00061354,0.0005105406,0.0006332317,0.0003827208],"category_scores_gemma":[0.0009781054,0.0002201567,0.0006539966,0.0003735108,0.0002896238,0.0004980773,0.0006598516,0.0006689905,0.0001951651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002964058,"about_ca_system_score_gemma":0.0002751364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266912,"about_ca_topic_score_gemma":0.001292543,"domain_scores_codex":[0.9997889,0.00006623911,0.000008787982,0.00005613915,0.00005091997,0.00002903807],"domain_scores_gemma":[0.9996081,0.0001947757,0.00004033792,0.00004172569,0.00007606269,0.00003900866],"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":[0.001082197,0.0005979884,0.02242099,0.0001322577,0.0002972482,0.0002417332,0.00008814195,0.6503062,0.1041707,0.003799322,0.002559911,0.2143033],"study_design_scores_gemma":[0.00000820459,0.00003587297,0.001140128,9.594374e-7,0.00001230431,0.00001211039,0.000004333505,0.9936635,0.003974359,0.00101752,0.0001260974,0.000004613035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.573051,0.0003023439,0.4237606,0.0002311046,0.00005946558,0.00003462311,0.0001601662,0.001406914,0.0009938823],"genre_scores_gemma":[0.9386094,0.00005011016,0.060224,0.00005330751,0.00001825272,0.00001540566,0.0002833641,0.00004696518,0.0006992404],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001266912,"threshold_uncertainty_score":0.005158603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04807439189777356,"score_gpt":0.3213951069901215,"score_spread":0.2733207150923479,"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."}}