{"id":"W2073735441","doi":"10.4161/sysb.28527","title":"Bridging in vivo and in vitro data from Japanese Toxicogenomics Project using network analyses","year":2014,"lang":"en","type":"article","venue":"Systems Biomedicine","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Toxicogenomics; Computational biology; Biology; Context (archaeology); In vivo; Gene; Bioinformatics; Gene expression; Genetics; Data mining; Computer science","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":[0.001989365,0.0004991526,0.0005684973,0.00253775,0.0005753203,0.0008727433,0.0004743148,0.0004048894,0.001192285],"category_scores_gemma":[0.003868858,0.0002173545,0.0008409952,0.003235906,0.000495827,0.0007072274,0.0009410133,0.0005823747,0.0002080052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008876637,"about_ca_system_score_gemma":0.001181388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0062555,"about_ca_topic_score_gemma":0.009959341,"domain_scores_codex":[0.9983233,0.0007521511,0.0001079052,0.0004148862,0.0002941219,0.0001076768],"domain_scores_gemma":[0.9965807,0.001494857,0.0006840275,0.0004860875,0.0005814502,0.0001728927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001565299,0.0006020344,0.4062007,0.00191907,0.00204024,0.001362181,0.0014865,0.1303457,0.2476699,0.01645648,0.004323842,0.1860281],"study_design_scores_gemma":[0.00004995736,0.0005531044,0.6716424,0.00009401175,0.001701345,0.000683827,0.001357075,0.2147862,0.0631437,0.02876584,0.01707771,0.0001449515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7472738,0.001234283,0.2362992,0.0006278028,0.00005736467,0.0001755002,0.009767013,0.0003971669,0.004167831],"genre_scores_gemma":[0.8984107,0.00124486,0.08209681,0.0001469294,0.00003118177,0.0003916756,0.01638959,0.00007523206,0.001212989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0062555,"threshold_uncertainty_score":0.01243818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04960028670814553,"score_gpt":0.3148135354164932,"score_spread":0.2652132487083477,"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."}}