{"id":"W2893226894","doi":"10.1186/s40694-018-0060-7","title":"A community-driven reconstruction of the Aspergillus niger metabolic network","year":2018,"lang":"en","type":"article","venue":"Fungal Biology and Biotechnology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Novo Nordisk Fonden; Novo Nordisk; Stichting voor de Technische Wetenschappen; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Villum Fonden","keywords":"SBML; Computer science; Aspergillus niger; Flux balance analysis; Metabolic network; Experimental data; Software; Metabolic engineering; Data mining; Computational biology; Biochemical engineering; Biology; Gene; Biotechnology; Markup language; XML; Programming language; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004142474,0.0007887062,0.0005562964,0.001575709,0.0004349762,0.000957933,0.001051606,0.001212743,0.00277596],"category_scores_gemma":[0.001701331,0.0004680748,0.00158777,0.00129815,0.0003153347,0.0006456006,0.0008668272,0.0006802441,0.0006358514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008277384,"about_ca_system_score_gemma":0.001282608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01992369,"about_ca_topic_score_gemma":0.01553624,"domain_scores_codex":[0.9998043,0.00005151501,0.000008808908,0.00005897623,0.00004073761,0.00003561197],"domain_scores_gemma":[0.9994932,0.0002311388,0.00004477838,0.00005961476,0.0001029882,0.00006818804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009192514,0.00004122297,0.004252824,0.00009359386,0.00005335168,0.0001980384,0.0000484133,0.9832867,0.003405311,0.002107725,0.0007694563,0.005651334],"study_design_scores_gemma":[0.00001015609,0.000008704083,0.0003987856,0.000008702449,0.000008350081,0.0000195509,0.00002586153,0.9970422,0.0003585525,0.001339906,0.0007731144,0.000006002591],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5958464,0.0009549946,0.3738543,0.000838099,0.0001458353,0.0001628031,0.01447263,0.004366082,0.009358873],"genre_scores_gemma":[0.8246709,0.0005814935,0.1573175,0.00008290313,0.00002817156,0.0001934895,0.01496837,0.00035544,0.001801758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01992369,"threshold_uncertainty_score":0.03961545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006447910474245026,"score_gpt":0.2168174278189424,"score_spread":0.2103695173446973,"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."}}