{"id":"W4416385581","doi":"10.1093/nar/gkaf1219","title":"DRESIS 2.0: the comprehensive landscape of drug resistance information","year":2025,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Reprogramming; Drug resistance; Mechanism (biology); Resistance (ecology); Drug discovery; Drug; Disease","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":[0.006265676,0.00256165,0.002478604,0.008005312,0.0009426812,0.005520365,0.002708847,0.001477915,0.02095254],"category_scores_gemma":[0.01503559,0.001762563,0.001802137,0.006358971,0.000573408,0.005134522,0.005363652,0.003070245,0.02515949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217307,"about_ca_system_score_gemma":0.003276284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001672338,"about_ca_topic_score_gemma":0.002880893,"domain_scores_codex":[0.9974104,0.0005145479,0.0002895649,0.0005375472,0.0009895436,0.0002585659],"domain_scores_gemma":[0.9934675,0.002443614,0.0008033425,0.00130339,0.001088863,0.0008933799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002986156,0.0002161838,0.01375579,0.006528062,0.001159258,0.0009569466,0.0007394389,0.004596501,0.0407708,0.01215672,0.7187286,0.1974055],"study_design_scores_gemma":[0.0005302793,0.0003649203,0.01130971,0.00119682,0.0006337878,0.001318816,0.0002884575,0.01447757,0.03576433,0.01770468,0.9159554,0.0004552121],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.02424756,0.01497211,0.06829348,0.0056602,0.001035692,0.0004810156,0.4978329,0.3566527,0.03082431],"genre_scores_gemma":[0.07463451,0.01031353,0.09563917,0.002542446,0.0004359072,0.0005284281,0.7820615,0.02820011,0.00564446],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02095254,"threshold_uncertainty_score":0.07009327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408996295996917,"score_gpt":0.3005641145720968,"score_spread":0.2864741516121276,"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."}}