{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008679113,0.0001246805,0.0001022134,0.00005689391,0.00005955866,0.00002116913,0.0001566779,0.000127815,0.00002199334],"category_scores_gemma":[0.00002516089,0.0001193335,0.00009511324,0.00007319442,0.00004468538,0.00001449364,0.00004326868,0.00005708378,0.0000247954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003557127,"about_ca_system_score_gemma":0.00001882047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002878608,"about_ca_topic_score_gemma":0.00000121662,"domain_scores_codex":[0.9992312,0.00000579959,0.0001736157,0.0003113793,0.0001387767,0.0001392709],"domain_scores_gemma":[0.9993861,0.00001008707,0.0001113015,0.0002161347,0.0002395139,0.00003689287],"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":[0.000138107,0.00003979152,0.0001065733,0.00001454475,0.0000531022,1.967086e-7,0.000003926913,0.00001744986,0.9963465,0.0001225462,0.00239012,0.0007671143],"study_design_scores_gemma":[0.0002890362,0.0001523946,0.0001153668,0.00001714063,0.00001672463,0.00001236149,0.00001673185,0.00007660587,0.9420038,0.0001121555,0.05703621,0.000151452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738005,0.00004817788,0.02286741,0.0005765132,0.0004517148,0.0003538895,0.0002127137,0.00007667475,0.001612423],"genre_scores_gemma":[0.9891893,0.0001604265,0.005169349,0.0002760301,0.001359132,0.0001076448,0.00260342,0.00001955915,0.001115127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05464609,"threshold_uncertainty_score":0.4866281,"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."}}