{"id":"W4282563525","doi":"10.1101/2022.06.08.495370","title":"Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; Vector Institute; Lunenfeld-Tanenbaum Research Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Vector Institute","keywords":"Computer science; Bayesian optimization; Workflow; Multi-objective optimization; Heuristic; Set (abstract data type); Bayesian probability; Data mining; Gaussian process; A priori and a posteriori; Machine learning; Process (computing); Mathematical optimization; Artificial intelligence; Gaussian; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007698987,0.001675969,0.001495785,0.001388906,0.0006385706,0.001648349,0.001523975,0.002209546,0.001981081],"category_scores_gemma":[0.01009719,0.001236784,0.001568986,0.001077337,0.001975797,0.001418979,0.002369022,0.002778762,0.0005890247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001883695,"about_ca_system_score_gemma":0.002486317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002686098,"about_ca_topic_score_gemma":0.003067968,"domain_scores_codex":[0.9981012,0.001078698,0.00006915886,0.0002114816,0.0004389676,0.0001005991],"domain_scores_gemma":[0.9957383,0.003063296,0.0004370756,0.0002117603,0.0003684113,0.0001811829],"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":[0.00005133433,0.0000462063,0.0003261853,0.0001196335,0.00005980651,0.00003284476,0.0000549414,0.9573156,0.001942593,0.01817978,0.0005258159,0.02134521],"study_design_scores_gemma":[0.000009590482,0.00001392275,0.00004831638,0.00001341786,0.000004293139,0.000006780348,0.000005743705,0.9897962,0.0004911033,0.009179479,0.0004242107,0.000007001853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002088442,0.0001532465,0.996884,0.0001231058,0.00001215234,0.00003567454,0.00002235257,0.0001265027,0.0005545199],"genre_scores_gemma":[0.127183,0.0003295926,0.8694903,0.0002622998,0.00005125811,0.0005403763,0.0001870165,0.0002852311,0.001670888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007698987,"threshold_uncertainty_score":0.04071659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0142208353852261,"score_gpt":0.237382862733925,"score_spread":0.2231620273486989,"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."}}