{"id":"W2964268061","doi":"10.1002/anie.201905005","title":"High‐Performance Nucleic Acid Sensors for Liquid Biopsy Applications","year":2019,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nucleic acid; Liquid biopsy; Chemistry; Nanotechnology; Computer science; Combinatorial chemistry; Materials science; Biochemistry; Biology; Genetics","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":[0.0006421048,0.000703201,0.0004204694,0.0005658649,0.0001910557,0.000937596,0.0009834644,0.001683715,0.004023898],"category_scores_gemma":[0.0006041575,0.0004955385,0.00037511,0.0006209414,0.000270283,0.0009919367,0.0004884925,0.001064331,0.004567562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000507461,"about_ca_system_score_gemma":0.0003109791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003337726,"about_ca_topic_score_gemma":0.0003502734,"domain_scores_codex":[0.9993199,0.0001157247,0.00003332173,0.000110575,0.0003765984,0.00004392005],"domain_scores_gemma":[0.9997661,0.00008287109,0.00003570917,0.00001537478,0.00008562598,0.00001432906],"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":[0.00009499759,0.00005725081,0.0001865973,0.001251767,0.00003591018,0.0001904715,0.00006429986,0.000807083,0.8339007,0.006856965,0.008737248,0.1478166],"study_design_scores_gemma":[0.00001522353,0.0002201851,0.0005065312,0.0001570041,0.00003887013,0.0008389355,0.00003995511,0.005797368,0.7288053,0.002382762,0.2611479,0.00004994766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0435604,0.2662571,0.6094654,0.0039584,0.003041784,0.0004625744,0.001221172,0.003144864,0.0688884],"genre_scores_gemma":[0.2892926,0.1543918,0.4811252,0.003528295,0.001190829,0.0007468474,0.001890534,0.0003335335,0.06750045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004023898,"threshold_uncertainty_score":0.01346129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007288789423184998,"score_gpt":0.2582010316349642,"score_spread":0.2509122422117792,"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."}}