{"id":"W2166372712","doi":"10.1093/bioinformatics/btu544","title":"Figmop: a profile HMM to identify genes and bypass troublesome gene models in draft genomes","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Gene; Genome; Computational biology; Hidden Markov model; Biology; Genetics; Computer science; Artificial intelligence","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":[0.001989793,0.001583304,0.001018675,0.002162927,0.0009502387,0.001716729,0.002554724,0.001823915,0.03044908],"category_scores_gemma":[0.009321917,0.001442965,0.001832816,0.002179207,0.0004780853,0.002882344,0.002333935,0.001781885,0.01715514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005975754,"about_ca_system_score_gemma":0.001290253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001928065,"about_ca_topic_score_gemma":0.003558948,"domain_scores_codex":[0.9993807,0.0001687508,0.00005561838,0.0002129136,0.0001167188,0.00006520406],"domain_scores_gemma":[0.9979073,0.001224595,0.0001616442,0.0003873234,0.0002037354,0.0001153453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003274865,0.000309138,0.01398697,0.00645885,0.0008384251,0.001692038,0.00156136,0.02418021,0.05947829,0.01378331,0.5208088,0.3536277],"study_design_scores_gemma":[0.0006429378,0.0004415102,0.01193078,0.0009240089,0.0004683243,0.002324306,0.0006616385,0.4036697,0.06878282,0.04595287,0.4638168,0.0003844026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01299164,0.0004252103,0.618445,0.0003965725,0.0002745355,0.0002872184,0.05344903,0.3105892,0.003141615],"genre_scores_gemma":[0.07956033,0.000905865,0.7634605,0.0004573224,0.000109166,0.001099249,0.1012557,0.04947151,0.003680347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03044908,"threshold_uncertainty_score":0.1018623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148272516669068,"score_gpt":0.2479729135171589,"score_spread":0.2331456618502521,"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."}}