{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001414052,0.0003494209,0.0003030835,0.0005832007,0.0002516264,0.0002801316,0.000129575,0.000233916,0.0008461105],"category_scores_gemma":[0.0001748315,0.0001503932,0.0004111445,0.0004796025,0.0001148801,0.0001316894,0.0001832456,0.0002312442,0.0004254188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002425011,"about_ca_system_score_gemma":0.0002639649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962202,"about_ca_topic_score_gemma":0.003843626,"domain_scores_codex":[0.9998826,0.000008345406,0.000008932403,0.00005506624,0.00002951284,0.0000155387],"domain_scores_gemma":[0.999826,0.00003851945,0.00006134978,0.000009191595,0.00002835813,0.00003651145],"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":[0.0002489361,0.00001665888,0.006610843,0.000114351,0.00002171683,0.0001625324,0.00009144888,0.0001421484,0.9889795,0.00004203202,0.0001346829,0.003435201],"study_design_scores_gemma":[0.00007640372,0.0006122528,0.7854105,0.00005309621,0.0002574429,0.001377904,0.0003932228,0.005390629,0.1921515,0.0001717838,0.01404586,0.0000593878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871922,0.001853351,0.002288075,0.00006388536,0.00001314166,0.00003655507,0.007588245,0.0001118398,0.0008527854],"genre_scores_gemma":[0.9596111,0.001183789,0.008498569,0.000130566,0.00001845217,0.00009089166,0.02819989,0.0000635501,0.002203239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001962202,"threshold_uncertainty_score":0.003901541,"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."}}