{"id":"W2808312973","doi":"10.1038/s41598-018-27500-3","title":"In planta proximity dependent biotin identification (BioID)","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Ontario Genomics; Genome Canada","keywords":"Proteome; Biotin; Computational biology; Identification (biology); Proteomics; Arabidopsis thaliana; Arabidopsis; Biology; Cell biology; Bioinformatics; Biochemistry; Botany; Gene","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":[0.000433327,0.0008536328,0.0005543238,0.0005577055,0.0008863198,0.001086464,0.0009043778,0.0013708,0.008480342],"category_scores_gemma":[0.0002531315,0.0005163456,0.0004052528,0.0005963342,0.0004881216,0.0009161688,0.001385533,0.001746379,0.01010038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007690207,"about_ca_system_score_gemma":0.0003850679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007957258,"about_ca_topic_score_gemma":0.001285961,"domain_scores_codex":[0.999535,0.00007905508,0.00002051921,0.0001687325,0.000127093,0.00006965617],"domain_scores_gemma":[0.9998109,0.00003425498,0.00002834629,0.00005769142,0.00002913825,0.00003972923],"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":[0.00007604351,0.00001077351,0.00008861641,0.0001224953,0.000005342928,0.00008095446,0.00003185536,0.00004218765,0.9938483,0.0007829273,0.0004347127,0.004475866],"study_design_scores_gemma":[0.00001224719,0.00006048779,0.0007506253,0.00002023752,0.00001666393,0.0008614428,0.00004337437,0.0008785132,0.9355508,0.0004782008,0.06131431,0.00001311121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.376532,0.02122207,0.5212873,0.004161933,0.001621341,0.0005539662,0.009879826,0.01074861,0.05399285],"genre_scores_gemma":[0.6531231,0.01025958,0.2482367,0.001133744,0.000117856,0.0004903372,0.01355082,0.001233064,0.07185479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008480342,"threshold_uncertainty_score":0.02836955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021038432504952,"score_gpt":0.2545490721283011,"score_spread":0.2443386878032516,"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."}}