{"id":"W2102099949","doi":"10.3389/fphar.2013.00028","title":"Mechanisms and insights into drug resistance in cancer","year":2013,"lang":"en","type":"article","venue":"Frontiers in Pharmacology","topic":"Cancer therapeutics and mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":660,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"National Cancer Institute; National Institutes of Health; Centre National de la Recherche Scientifique; Leukemia and Lymphoma Society","keywords":"Drug resistance; Mechanism (biology); Cancer; Drug; Medicine; Computational biology; Multiple drug resistance; Bioinformatics; Cancer drugs; Biology; Pharmacology; Genetics; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.00008925672,0.0001346728,0.0001754257,0.0001167306,0.00003487129,0.00001121582,0.0001462634,0.0001182972,0.00006766862],"category_scores_gemma":[0.000003470159,0.0001379564,0.00002432802,0.0001161793,0.00006880772,0.000007004335,0.00008282445,0.0001263601,0.000002613423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006270556,"about_ca_system_score_gemma":0.00006103149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002168026,"about_ca_topic_score_gemma":0.001514327,"domain_scores_codex":[0.9990908,0.00007073631,0.0001768884,0.0003436722,0.0000591836,0.0002587259],"domain_scores_gemma":[0.9997321,0.000005401327,0.00004807755,0.0001159103,0.00002995021,0.00006857522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001297751,0.00006023904,0.000781291,0.00002630072,0.00004317317,0.000008313345,0.0007283377,0.00002472632,0.9587045,0.0006366014,0.03193357,0.006923135],"study_design_scores_gemma":[0.003829747,0.0001296699,0.002184592,0.00003880323,0.00003296489,0.000002575697,0.0005742477,0.0008616261,0.6298218,0.09021283,0.2717694,0.0005417792],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656919,0.02546587,0.004351383,0.001389781,0.002144351,0.0004716199,0.000002952714,0.000009364455,0.0004728072],"genre_scores_gemma":[0.9793736,0.007881355,0.008542149,0.002343066,0.0000776872,0.0003780936,0.00001022744,0.00002284686,0.001370969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3288828,"threshold_uncertainty_score":0.5625697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00585164367717898,"score_gpt":0.2606349100861152,"score_spread":0.2547832664089362,"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."}}