{"id":"W4387343780","doi":"10.1016/j.crmeth.2023.100599","title":"RECOVER identifies synergistic drug combinations in vitro through sequential model optimization","year":2023,"lang":"en","type":"article","venue":"Cell Reports Methods","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Université de Montréal; Institute for Research in Immunology and Cancer; Mila - Quebec Artificial Intelligence Institute","funders":"Engineering and Physical Sciences Research Council; Bill and Melinda Gates Foundation","keywords":"In silico; Computer science; Benchmarking; Machine learning; Artificial intelligence; Selection (genetic algorithm); Drug; Drug discovery; Computational biology; Bioinformatics; Biology; Pharmacology","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":[0.00404638,0.0002282378,0.0003286306,0.0004481483,0.0001831988,0.0002995189,0.0004971689,0.00008956766,0.00001843334],"category_scores_gemma":[0.0008974896,0.0002504812,0.000149215,0.00195015,0.00007069693,0.001246489,0.0005280516,0.0002323686,0.0000188443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001810109,"about_ca_system_score_gemma":0.0003435176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008951867,"about_ca_topic_score_gemma":0.000004220431,"domain_scores_codex":[0.9963943,0.001040327,0.0008318014,0.0008102539,0.0005252491,0.000398016],"domain_scores_gemma":[0.9973446,0.001166647,0.0003729787,0.0008396648,0.0001939448,0.00008212769],"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":[0.000004677408,0.00008705684,0.00001242952,0.00003063398,0.000009322845,0.0001800973,0.0008738502,0.9809056,0.008231871,0.004853681,0.000872196,0.003938579],"study_design_scores_gemma":[0.0001651507,0.000004807447,0.00008392206,0.00001472215,0.00001168141,0.00002209091,0.00003306679,0.7997774,0.04548933,0.1539325,0.0002501933,0.0002151002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0106543,0.00007891109,0.9790525,0.0003617611,0.002078398,0.0002921522,0.000004823753,0.0003831183,0.007093974],"genre_scores_gemma":[0.1078998,0.00003361056,0.8880859,0.0001089415,0.00004294733,0.00007191839,0.00007994753,0.0000297141,0.003647224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1811282,"threshold_uncertainty_score":0.9999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04781760163582092,"score_gpt":0.3688485393359173,"score_spread":0.3210309377000964,"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."}}