{"id":"W3014471733","doi":"10.1101/2020.04.01.020479","title":"A high-density human mitochondrial proximity interaction network","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mitochondrial Function and Pathology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Ontario Genomics; United Mitochondrial Disease Foundation; Genome Canada","keywords":"Mitochondrion; Biology; Cytosol; Organelle; Proteome; Cell biology; Cellular compartment; Mitochondrial matrix; Biotinylation; Mitochondrial DNA; Mitochondrial disease; mitochondrial fusion; Gene isoform; Computational biology; Biochemistry; Gene; Cell; Enzyme","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":[0.0001945731,0.0002217428,0.000300633,0.001127502,0.0004003938,0.0006154833,0.000307019,0.0003165983,0.002415654],"category_scores_gemma":[0.0008673172,0.0001863037,0.0002673635,0.001691905,0.0001970189,0.0005002076,0.0005154546,0.0002252934,0.0005540671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00056773,"about_ca_system_score_gemma":0.0003203664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002751508,"about_ca_topic_score_gemma":0.00277266,"domain_scores_codex":[0.9995381,0.00008482434,0.00001709153,0.0001461942,0.0001755027,0.00003842149],"domain_scores_gemma":[0.9995586,0.00017222,0.00008857565,0.00004009813,0.000100148,0.0000404525],"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":[0.00116639,0.0002447034,0.07452423,0.001231436,0.0004595311,0.003479756,0.001076716,0.2569288,0.4186831,0.04997931,0.02445209,0.167774],"study_design_scores_gemma":[0.00005233261,0.0001627999,0.08240067,0.00005592432,0.00009939616,0.002484508,0.0004283818,0.7787624,0.0595875,0.02479288,0.05111326,0.0000599553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.786878,0.001596922,0.1887348,0.0005431112,0.00002676114,0.0001584911,0.009453731,0.001145456,0.01146261],"genre_scores_gemma":[0.9300427,0.0005062682,0.05683137,0.00006330435,0.00002313417,0.0001339072,0.009041368,0.00004875043,0.003309168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002751508,"threshold_uncertainty_score":0.008081198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718399636518591,"score_gpt":0.2360060738711313,"score_spread":0.2188220775059454,"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."}}