{"id":"W4384695742","doi":"10.1101/2023.07.14.548964","title":"Metabolic Interactive Nodular Network for Omics (MINNO): Refining and investigating metabolic networks based on empirical metabolomics data","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; International Microbiome Centre, University of Calgary; National Institutes of Health; Canadian Institutes of Health Research; Alberta Innovates","keywords":"Metabolomics; Computational biology; Metabolic network; Biology; Genomics; Context (archaeology); Metabolic pathway; Systems biology; Computer science; Bioinformatics; Genome; Genetics; 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":[0.001660492,0.001369672,0.0007030442,0.001994408,0.0005343606,0.001086482,0.0008780596,0.0005512126,0.00935775],"category_scores_gemma":[0.00290433,0.0004268027,0.001272889,0.001380187,0.0003101938,0.00103175,0.001426709,0.001026696,0.0008863693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006650187,"about_ca_system_score_gemma":0.0007953963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001684563,"about_ca_topic_score_gemma":0.003225425,"domain_scores_codex":[0.9995725,0.0001280859,0.00002236835,0.0001194094,0.0001297963,0.00002787134],"domain_scores_gemma":[0.9987432,0.0006887522,0.0001938336,0.0001501812,0.0001558501,0.00006819987],"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":[0.001812562,0.0003491856,0.03720909,0.005375448,0.001579324,0.001637696,0.001380812,0.3700847,0.2313073,0.05964132,0.07959735,0.2100253],"study_design_scores_gemma":[0.0001221336,0.000138458,0.009262126,0.0002173459,0.0001977495,0.0002638923,0.0001956142,0.8452688,0.0530904,0.032418,0.05867396,0.0001515495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1168994,0.0008406822,0.786853,0.001352612,0.0002585148,0.0002667858,0.0456309,0.04258044,0.005317671],"genre_scores_gemma":[0.3242885,0.0009350693,0.6428609,0.0003325563,0.00005479202,0.0006695551,0.02559063,0.003115424,0.002152503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00935775,"threshold_uncertainty_score":0.03130478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03871427110693103,"score_gpt":0.272727643671619,"score_spread":0.234013372564688,"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."}}