{"id":"W3203248540","doi":"10.1007/978-1-0716-1740-3_14","title":"Chemical–Genetic Interactions as a Means to Characterize Drug Synergy","year":2021,"lang":"en","type":"review","venue":"Methods in molecular biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"","keywords":"Leverage (statistics); Mechanism (biology); Computational biology; Computer science; Genomics; Genome; Data science; Biology; Genetics; Gene; Artificial intelligence; Epistemology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008713185,0.0008354397,0.002391956,0.0004948465,0.00005759623,0.00004347611,0.0009392157,0.0008609106,0.0001660738],"category_scores_gemma":[0.0008122805,0.0008198798,0.001327371,0.00106227,0.0001360074,0.000002217793,0.0009351512,0.0005696727,0.0000624455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367586,"about_ca_system_score_gemma":0.0004306486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005124357,"about_ca_topic_score_gemma":0.00004660117,"domain_scores_codex":[0.9924789,0.003697176,0.001167879,0.00174534,0.000139498,0.0007711889],"domain_scores_gemma":[0.9973215,0.0001546039,0.0003969269,0.001691008,0.0001436327,0.0002923677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009564176,0.00009264772,0.000006377049,0.0004605456,0.0008006234,0.00008421962,0.00002099375,0.00004541231,0.2486462,0.0001609044,0.0003325257,0.74934],"study_design_scores_gemma":[0.0001340574,0.00005928105,0.000001427411,0.0008906256,0.0006020457,0.000218415,0.0000130197,0.00001210522,0.02161053,0.000104863,0.9755831,0.0007705819],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003015515,0.829444,0.1688047,0.00007147091,0.0004960814,0.0005036565,0.00003674822,0.00002182623,0.0003199666],"genre_scores_gemma":[0.00001091748,0.6985844,0.2982083,0.000444338,0.0003169856,0.0004905307,0.001004935,0.0001365419,0.0008031337],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9752505,"threshold_uncertainty_score":0.9994252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756061491471542,"score_gpt":0.4003648136021292,"score_spread":0.3728041986874138,"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."}}