{"id":"W4409690336","doi":"10.1158/1538-7445.am2025-504","title":"Abstract 504: A comparative analysis of statistical and machine learning approaches to predict drug resistance based on synergistic genetic alterations","year":2025,"lang":"en","type":"article","venue":"Cancer Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Computational biology; Resistance (ecology); Drug; Machine learning; Computer science; Artificial intelligence; Biology; Pharmacology; Ecology","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":[0.005730776,0.001112741,0.0008274797,0.003597154,0.000252551,0.001064354,0.0006405971,0.0005727303,0.002941945],"category_scores_gemma":[0.01087247,0.0001816622,0.001738956,0.001170938,0.0002906864,0.0006652882,0.0006284884,0.0004739011,0.0006521269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007805508,"about_ca_system_score_gemma":0.0008406226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002567227,"about_ca_topic_score_gemma":0.001947908,"domain_scores_codex":[0.9973032,0.001407118,0.0002357171,0.0004411057,0.0005010808,0.0001116329],"domain_scores_gemma":[0.9883377,0.009689159,0.0006057174,0.0005687012,0.0006058708,0.0001928915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003924478,0.0009457195,0.4955132,0.0005281772,0.003362444,0.0003485261,0.0001431163,0.237633,0.007864322,0.001246019,0.004800514,0.2436904],"study_design_scores_gemma":[0.0001111954,0.00152307,0.1162125,0.00005082375,0.0005235986,0.0003813406,0.00008288046,0.8723962,0.004533521,0.002406507,0.001715067,0.00006334716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9077798,0.001690195,0.0786404,0.0006710949,0.0001094872,0.0001971449,0.004446342,0.002284879,0.004180556],"genre_scores_gemma":[0.9693385,0.0002053495,0.02610444,0.0001123678,0.00006183316,0.0001067548,0.003381427,0.00009659331,0.000592818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005730776,"threshold_uncertainty_score":0.03030759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1965778109701726,"score_gpt":0.4377918090231742,"score_spread":0.2412139980530016,"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."}}