{"id":"W4225531330","doi":"10.1158/1535-7163.mct-21-0442","title":"Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence–Enabled Studies of Acute Phosphoproteomic Changes","year":2022,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"National Institute for Health and Care Research; Cancer Research UK; Wellcome Trust; National Institute on Handicapped Research","keywords":"Medicine; Drug; KRAS; Combination therapy; Targeted therapy; EGFR inhibitors; Pharmacology; Oncology; Computational biology; Cancer research; Cancer; Internal medicine; Biology; Epidermal growth factor receptor; Colorectal cancer","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":[0.0004518228,0.0001228141,0.0001991233,0.0001150977,0.0000800255,0.000008865462,0.0001097894,0.00005109864,0.00001249996],"category_scores_gemma":[0.00001898747,0.0001343005,0.00004782607,0.0001945386,0.0001170835,0.000003687849,0.000117026,0.00008427248,3.735127e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008251242,"about_ca_system_score_gemma":0.0002011268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008543899,"about_ca_topic_score_gemma":0.00004525543,"domain_scores_codex":[0.9989712,0.0001868496,0.0002915603,0.0002197958,0.000200068,0.000130565],"domain_scores_gemma":[0.9994462,0.00003729783,0.0002030411,0.0001634976,0.0001304611,0.00001953659],"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":[0.002283521,0.0001285313,0.001206679,0.0000158964,0.0003657111,0.000002295135,0.001117472,0.003426596,0.9839098,0.0006080231,0.000006516047,0.006928973],"study_design_scores_gemma":[0.0008853316,0.0006430178,0.001266426,0.00001978113,0.00008132185,0.000003778885,0.0007042962,0.001798604,0.9915966,0.002405311,0.0004680674,0.0001275058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872251,0.00974721,0.001738862,0.0003845805,0.0001868392,0.000403296,0.0003062195,0.000003443713,0.000004474825],"genre_scores_gemma":[0.9931846,0.005677279,0.0005870817,0.0003058029,0.00002696726,0.0001198456,0.00006835442,0.00002013141,0.000009982639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007686772,"threshold_uncertainty_score":0.5476615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04739014557391607,"score_gpt":0.3206153187094599,"score_spread":0.2732251731355438,"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."}}