{"id":"W4214536715","doi":"10.1007/978-3-030-91836-1_11","title":"Network Approaches for Precision Oncology","year":2022,"lang":"en","type":"article","venue":"Advances in experimental medicine and biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Interpretability; Computer science; Computational biology; Data science; Relevance (law); Precision medicine; Personalized medicine; Field (mathematics); Machine learning; Bioinformatics; Artificial intelligence; Medicine; Biology","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":[0.00288883,0.001174766,0.001423197,0.002881969,0.001045252,0.002681676,0.001836089,0.001232962,0.01078658],"category_scores_gemma":[0.008805373,0.0005584517,0.001398913,0.002172948,0.001937129,0.004863437,0.003340709,0.002669448,0.001656837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002095278,"about_ca_system_score_gemma":0.001497322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002654953,"about_ca_topic_score_gemma":0.002326563,"domain_scores_codex":[0.9982784,0.0007153507,0.00006196316,0.0005270969,0.0003388549,0.00007840979],"domain_scores_gemma":[0.994723,0.003282139,0.0003761739,0.0009739609,0.0004281884,0.0002166785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006624211,0.00003749413,0.0009749014,0.0002567966,0.0001843197,0.00007612714,0.000124466,0.0664767,0.001123595,0.8633292,0.00645292,0.06089722],"study_design_scores_gemma":[0.000009907995,0.00001533176,0.0002303468,0.00003740322,0.00005053383,0.00003053583,0.00003876684,0.08956694,0.0003257334,0.8971627,0.01252204,0.000009683536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00339111,0.00175478,0.9801463,0.002766772,0.0002675009,0.00007441547,0.0008603975,0.0003597875,0.01037891],"genre_scores_gemma":[0.3895735,0.008340425,0.5699079,0.001911327,0.001619144,0.001025291,0.003094679,0.0004227016,0.02410517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01078658,"threshold_uncertainty_score":0.03608465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04528567086769296,"score_gpt":0.3575088423764896,"score_spread":0.3122231715087966,"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."}}