{"id":"W2950796639","doi":"10.12688/f1000research.19592.1","title":"Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz","year":2019,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"National Human Genome Research Institute; National Institutes of Health; National Cancer Institute; RWTH Aachen University","keywords":"Open peer review; Plant biology; Visualization; Neuroscience; Computational biology; Physiology; Biology; Data science; Medicine; Computer science; Artificial intelligence","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":[0.001644688,0.001477354,0.0007084882,0.002996066,0.0005214044,0.001721843,0.001258813,0.00127764,0.01991821],"category_scores_gemma":[0.003297175,0.0005958254,0.001595424,0.001511217,0.0004437568,0.001475966,0.002118754,0.001971024,0.00285269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009767779,"about_ca_system_score_gemma":0.001296208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004447691,"about_ca_topic_score_gemma":0.005271128,"domain_scores_codex":[0.9994949,0.0001440221,0.00003790276,0.0001122064,0.0001636855,0.00004725279],"domain_scores_gemma":[0.998715,0.0008193711,0.0001078117,0.0001670499,0.0001190247,0.00007161931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003240678,0.0003812959,0.01475195,0.005516058,0.001413896,0.002764256,0.002872558,0.1350992,0.1417439,0.09705931,0.3396584,0.2554986],"study_design_scores_gemma":[0.0007448419,0.0001427044,0.01070661,0.0004533158,0.0002324942,0.0008651906,0.0003642123,0.4029936,0.09917436,0.06415959,0.4198858,0.0002772393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05409091,0.001759177,0.5400096,0.003689857,0.0006213039,0.0003339361,0.08502205,0.2978555,0.01661766],"genre_scores_gemma":[0.3008411,0.003633241,0.552928,0.001401844,0.0001712343,0.001628049,0.102822,0.02584046,0.01073404],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01991821,"threshold_uncertainty_score":0.06663305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01904124637757594,"score_gpt":0.3036544110383194,"score_spread":0.2846131646607434,"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."}}