{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002451304,0.0001222818,0.000178818,0.00004819329,0.0002373776,0.000003304514,0.0002201525,0.0005394149,0.00001058204],"category_scores_gemma":[0.00011606,0.00008399534,0.00005675076,0.0001979154,0.001007044,0.000002263151,0.000226333,0.0002870811,0.000003863872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000306528,"about_ca_system_score_gemma":0.00002021631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004637893,"about_ca_topic_score_gemma":0.00009210288,"domain_scores_codex":[0.9992051,0.0001747284,0.0001771836,0.0002130675,0.00002323661,0.0002067496],"domain_scores_gemma":[0.9994007,0.000004982231,0.00009489138,0.0004148846,0.00006288226,0.0000216594],"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":[0.00003969862,0.00001733966,0.008266917,0.000005790562,0.00006069889,6.913718e-8,0.00001821601,0.000004792202,0.973061,0.00331518,0.0003741454,0.0148361],"study_design_scores_gemma":[0.0002183351,0.0003903113,0.01206575,0.00001219616,0.00002975431,0.0001970496,0.00003717024,0.00001507795,0.8925612,0.0004975769,0.09385211,0.0001234869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959815,0.00161872,0.0003067851,0.0007466681,0.001023735,0.0001099993,0.00002054979,0.00003049033,0.0001615192],"genre_scores_gemma":[0.9975092,0.0005986833,0.0009749783,0.0001372453,0.0006141093,0.000005080345,0.00001629295,0.000008188955,0.0001361802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09347796,"threshold_uncertainty_score":0.4160462,"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."}}