{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002474257,0.0003771323,0.0002708105,0.0005600925,0.0002617989,0.0008805076,0.0003580973,0.0006189455,0.001213897],"category_scores_gemma":[0.002018889,0.0002324136,0.0002993893,0.0004263891,0.0006665324,0.0004846234,0.0006816162,0.0007319761,0.0004358206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035752,"about_ca_system_score_gemma":0.0004961865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005293033,"about_ca_topic_score_gemma":0.0009621195,"domain_scores_codex":[0.9993749,0.0002280166,0.00005303116,0.000151444,0.0001536338,0.00003889252],"domain_scores_gemma":[0.9990397,0.0003268703,0.0001834382,0.0002493116,0.000146352,0.00005434222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008114445,0.000233455,0.04286231,0.0002736953,0.0001175571,0.0004212891,0.0002320415,0.006758146,0.8625036,0.002191513,0.001220546,0.0823744],"study_design_scores_gemma":[0.00009882393,0.001490525,0.04312235,0.00009116442,0.0001889854,0.00350626,0.0002201869,0.03109137,0.8957158,0.004749723,0.01967242,0.00005231144],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.7704568,0.008858498,0.2092042,0.001288599,0.0002687676,0.0005421615,0.003223903,0.001134152,0.005022914],"genre_scores_gemma":[0.8560982,0.002489677,0.1370649,0.0003752379,0.0000690871,0.00025323,0.002381434,0.0001427496,0.00112541],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002474257,"threshold_uncertainty_score":0.01308531,"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."}}