{"id":"W2103592640","doi":"10.1186/1471-2105-15-229","title":"SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models","year":2014,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Concordia University","funders":"Genome Alberta; Genome Canada; McGill University","keywords":"Gene prediction; Genome; Computational biology; Gene; Biology; Genetics; Homology (biology); Gene Annotation; RNA-Seq; Genomics; DNA microarray; Transcriptome; Gene expression","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":[0.001012379,0.00187911,0.0008520879,0.0008048943,0.0006646879,0.001214262,0.001651995,0.0008538357,0.005839421],"category_scores_gemma":[0.001977849,0.001015741,0.001845665,0.0005505623,0.0004857724,0.001148239,0.001126922,0.001129534,0.002118894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008548607,"about_ca_system_score_gemma":0.00127084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005221676,"about_ca_topic_score_gemma":0.009092707,"domain_scores_codex":[0.9996527,0.00005553673,0.00001778253,0.0001353783,0.00009398822,0.00004454669],"domain_scores_gemma":[0.9992348,0.0004613609,0.00008697129,0.00008394337,0.00007852266,0.00005445722],"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":[0.003588993,0.0006342024,0.03736796,0.002479083,0.001233428,0.001334338,0.0008646796,0.3456333,0.2210734,0.007326598,0.09121015,0.2872538],"study_design_scores_gemma":[0.0001535883,0.000134634,0.002323945,0.00003876926,0.00005392525,0.00007469272,0.00005155816,0.9604293,0.02905594,0.00241476,0.005221259,0.00004772035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2677917,0.0008130862,0.5620967,0.0004244747,0.0002220622,0.0005035611,0.02423807,0.1396027,0.004307696],"genre_scores_gemma":[0.4220746,0.000530599,0.513541,0.0003570952,0.00006574882,0.0008492441,0.0488145,0.01010781,0.003659215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005839421,"threshold_uncertainty_score":0.01953477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008581434321299,"score_gpt":0.2237495654642957,"score_spread":0.2036637511210827,"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."}}