{"id":"W1978802080","doi":"10.1089/cmb.2007.0229","title":"Computing Knock-Out Strategies in Metabolic Networks","year":2008,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Computation; Computer science; Block (permutation group theory); Computational complexity theory; Metabolic network; Algorithm; Theoretical computer science; Mathematics; Computational biology; Biology; Combinatorics","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":[0.0008691421,0.001112555,0.0009888762,0.000778212,0.0004659276,0.001206792,0.001218272,0.0009076907,0.002128013],"category_scores_gemma":[0.005130931,0.0006836323,0.0007528834,0.0003926103,0.001140394,0.001960139,0.001188386,0.00101034,0.0002998515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00138515,"about_ca_system_score_gemma":0.001041448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002750223,"about_ca_topic_score_gemma":0.00399873,"domain_scores_codex":[0.9995676,0.0001319205,0.00003161274,0.0001028049,0.00008304711,0.00008306184],"domain_scores_gemma":[0.9978618,0.00161148,0.0001221212,0.0001723996,0.0001214324,0.000110659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001668707,0.00004458171,0.0007393528,0.00006540244,0.00002534222,0.00007775907,0.00004687346,0.9570078,0.005983224,0.02342859,0.0002647501,0.01214939],"study_design_scores_gemma":[0.00002443897,0.00002661616,0.0000915109,0.000004906503,0.00001098376,0.00001014195,0.00001311198,0.9595568,0.003642429,0.03640953,0.0002022393,0.000007267798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.410721,0.000176777,0.5819499,0.0002141463,0.00002292476,0.00008957193,0.0004647358,0.001786982,0.004573971],"genre_scores_gemma":[0.8487899,0.0001525441,0.148683,0.00007149824,0.000008091531,0.0001520616,0.000625978,0.0001946889,0.001322189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002750223,"threshold_uncertainty_score":0.01005006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034546835111357,"score_gpt":0.2563395280575266,"score_spread":0.245994059706413,"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."}}