{"id":"W3093585277","doi":"10.1093/bioinformatics/btaa906","title":"Leveraging heterogeneous network embedding for metabolic pathway prediction","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Compute Canada; Genome Canada","keywords":"Computer science; Embedding; Computational biology; Artificial intelligence; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002026574,0.0002053755,0.0002106225,0.000021951,0.0001832237,0.00007691723,0.0002152963,0.0001375177,0.00001033996],"category_scores_gemma":[0.00005345149,0.0001957751,0.0001626033,0.0001000324,0.00003303133,0.00001400655,0.0001318283,0.0000945852,0.00002262064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009689087,"about_ca_system_score_gemma":0.00006589988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.193169e-7,"about_ca_topic_score_gemma":9.032051e-7,"domain_scores_codex":[0.9987836,0.00001319289,0.000506335,0.0001650203,0.0001236095,0.000408165],"domain_scores_gemma":[0.9992979,0.00001548003,0.0001922979,0.000244247,0.00007206401,0.0001780738],"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":[0.0007507253,0.0001183425,0.001958003,0.001189289,0.001245429,0.000005157159,0.007334912,0.5334431,0.02006401,0.003536773,0.153354,0.2770002],"study_design_scores_gemma":[0.0007565612,0.00031277,0.00004357364,0.00001841956,0.00004110425,0.00001989386,0.0002171884,0.5996467,0.006201235,0.0001870376,0.3922528,0.0003027473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05874876,0.001572676,0.9349934,0.0002899739,0.000837795,0.000922807,0.0001844881,0.0001076298,0.002342481],"genre_scores_gemma":[0.893571,0.000313831,0.09703089,0.004837081,0.003037032,0.00007947782,0.0009125518,0.00006264792,0.0001554415],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8379625,"threshold_uncertainty_score":0.7983476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01771528430364131,"score_gpt":0.2299493462855756,"score_spread":0.2122340619819343,"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."}}