{"id":"W2258535515","doi":"10.1158/2159-8290.cd-15-1336","title":"Application of Sequencing, Liquid Biopsies, and Patient-Derived Xenografts for Personalized Medicine in Melanoma","year":2015,"lang":"en","type":"article","venue":"Cancer Discovery","topic":"Melanoma and MAPK Pathways","field":"Biochemistry, Genetics and Molecular Biology","cited_by":230,"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; Wellcome","keywords":"Melanoma; Precision medicine; Medicine; Personalized medicine; Liquid biopsy; Targeted therapy; Exome sequencing; Biopsy; Oncology; Cancer research; Exome; Internal medicine; Mutation; Bioinformatics; Pathology; Cancer; Biology; Gene","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":[0.00008082065,0.00009050785,0.0001503305,0.00003428381,0.00001489069,0.000004444446,0.00006116495,0.0000705493,0.000002355235],"category_scores_gemma":[0.00004458859,0.00007667702,0.00002698636,0.00005196734,0.0001109603,0.000007494504,0.00003572911,0.00002439009,1.99477e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003189113,"about_ca_system_score_gemma":0.0001428762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003385498,"about_ca_topic_score_gemma":0.0001582525,"domain_scores_codex":[0.9994183,0.0000189713,0.0001614086,0.0002093866,0.00007164486,0.0001202987],"domain_scores_gemma":[0.9996396,0.000008242212,0.00008964619,0.0001429858,0.00006213395,0.00005744465],"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":[0.0005064385,0.00001491369,0.003333046,0.00005338629,0.00001949949,6.171129e-7,0.0005866466,0.00001992973,0.9925637,0.0002903626,0.0009526859,0.00165874],"study_design_scores_gemma":[0.002882955,0.001888289,0.001234208,0.00007951388,0.00003277088,0.000007200847,0.001559996,0.0001219175,0.9415138,0.0001972529,0.05026153,0.0002205998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901218,0.005998166,0.002987881,0.0001425803,0.0001317162,0.0002939916,0.0000764054,0.000003802369,0.0002435962],"genre_scores_gemma":[0.9988398,0.0002691273,0.0001553605,0.0001669527,0.0001480604,0.0001753902,0.0001382192,0.00001126427,0.00009584367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05104997,"threshold_uncertainty_score":0.3126798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02417063816839317,"score_gpt":0.2713803003633998,"score_spread":0.2472096621950067,"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."}}