{"id":"W2474174075","doi":"10.1371/journal.pone.0158784","title":"Genome Wide Identification and Functional Prediction of Long Non-Coding RNAs Responsive to Sclerotinia sclerotiorum Infection in Brassica napus","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Core Research for Evolutional Science and Technology; Natural Sciences and Engineering Research Council of Canada; Department of Biotechnology, Ministry of Science and Technology, India; University of Alberta","keywords":"Sclerotinia sclerotiorum; Biology; Gene; Genetics; Sclerotinia; Brassica; Biotic stress; Long non-coding RNA; microRNA; Genome; Intergenic region; Gene knockdown; Computational biology; Abiotic stress; RNA; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003524317,0.0001028141,0.0001297087,0.0002343494,0.0000600422,0.00001878999,0.00006380511,0.0001401655,0.0000290566],"category_scores_gemma":[0.0004199345,0.0001004789,0.00003382536,0.0002227232,0.00005251683,0.000015616,0.0001091311,0.00009059395,0.00001686355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008924719,"about_ca_system_score_gemma":0.00007925039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002815696,"about_ca_topic_score_gemma":0.00009653834,"domain_scores_codex":[0.998807,0.0000704739,0.0002625812,0.0003651499,0.0002922232,0.000202635],"domain_scores_gemma":[0.9993129,0.00002496445,0.00009336421,0.0002534052,0.0002293349,0.00008604781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003028658,0.0001672282,0.008136509,0.00003717138,0.00007901679,0.000001117854,0.00002844316,0.00004464309,0.9901612,0.00001303804,0.00003698574,0.0009917362],"study_design_scores_gemma":[0.0004607253,0.0002135168,0.3768023,0.0001430042,0.00001463672,0.00000197678,0.00000474479,0.00004277332,0.6221989,0.00002268525,0.00003336002,0.00006130793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9069272,0.0000527693,0.09195921,0.000398343,0.00006236166,0.0005046341,0.00002613933,0.000010896,0.00005848191],"genre_scores_gemma":[0.9986541,0.0003358723,0.0002165075,0.00004172189,0.0001066612,0.0001065337,0.00004805583,0.00002229015,0.000468287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3686658,"threshold_uncertainty_score":0.409741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02874815943037265,"score_gpt":0.2391532772711676,"score_spread":0.210405117840795,"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."}}