{"id":"W4400086862","doi":"10.1093/bib/bbae299","title":"Optimizing <i>in silico</i> drug discovery: simulation of connected differential expression signatures and applications to benchmarking","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"Horizon 2020 Framework Programme; Association Nationale de la Recherche et de la Technologie; European Commission","keywords":"Benchmarking; Computer science; Data mining; Machine learning; Pairwise comparison; Benchmark (surveying); In silico; Artificial intelligence; 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":[0.002036505,0.000709668,0.0008590099,0.0007073274,0.0004797569,0.0008920621,0.001456526,0.001194104,0.0023825],"category_scores_gemma":[0.008384108,0.0003672246,0.001095166,0.0009859608,0.0008365553,0.0007550818,0.0008713673,0.001060939,0.0003735815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580327,"about_ca_system_score_gemma":0.001453684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006109471,"about_ca_topic_score_gemma":0.003977973,"domain_scores_codex":[0.9993281,0.0003478464,0.00003305783,0.00009305296,0.0001422,0.00005570516],"domain_scores_gemma":[0.9953533,0.003576471,0.0003007822,0.0003536432,0.0002816978,0.000134082],"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":[0.00002695605,0.00002297611,0.001055955,0.00003394738,0.00001979616,0.0000249642,0.00002134999,0.9913066,0.0007330053,0.003737133,0.000231628,0.002785685],"study_design_scores_gemma":[0.000006414286,0.00001139561,0.0001019131,0.000003003997,0.0000031518,0.000006531508,0.000003727608,0.9966077,0.0005526356,0.002471878,0.0002286328,0.000003081738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1796955,0.0002710522,0.8088079,0.0006986086,0.00007557295,0.0002046536,0.000730554,0.001471326,0.008044883],"genre_scores_gemma":[0.7830991,0.000303352,0.2126442,0.0002878424,0.00003105754,0.0008088743,0.001128651,0.0003242538,0.001372649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006109471,"threshold_uncertainty_score":0.01214784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005984702884207795,"score_gpt":0.2397097968206889,"score_spread":0.2337250939364811,"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."}}