{"id":"W4250999655","doi":"10.1002/ange.201905005","title":"High‐Performance Nucleic Acid Sensors for Liquid Biopsy Applications","year":2019,"lang":"en","type":"article","venue":"Angewandte Chemie","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nucleic acid; Liquid biopsy; Microfluidics; Tissue sample; Nucleic acid quantitation; Nanotechnology; Biosensor; Raman spectroscopy; Surface plasmon resonance; Computational biology; Sample preparation; Cancer biomarkers; Chemistry; Materials science; Biomedical engineering; Biology; Cancer; Chromatography; Biochemistry; Medicine; Genetics; Nanoparticle","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":[0.00005830711,0.0001300668,0.0001225783,0.00002141706,0.00006017749,0.00001864567,0.0001585611,0.0001202944,0.00004266922],"category_scores_gemma":[0.0000165344,0.0001333208,0.00007439568,0.00006358285,0.00003553139,0.000003266752,0.00006709886,0.00004570029,0.00008019779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002032069,"about_ca_system_score_gemma":0.00005161996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006836779,"about_ca_topic_score_gemma":0.000003073731,"domain_scores_codex":[0.999276,0.000002953645,0.0001378295,0.0003023384,0.00005884618,0.0002220031],"domain_scores_gemma":[0.9993854,0.00001513191,0.00006059717,0.0003940153,0.0000773275,0.00006750852],"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":[0.0001165116,0.0000302092,0.0003692568,0.00004830266,0.00002637474,2.013267e-7,0.00001187986,0.00006410099,0.9956995,0.000143757,0.003104977,0.0003849601],"study_design_scores_gemma":[0.0004578987,0.0001953107,0.0001400112,0.000007467784,0.00001331383,0.000005142417,0.00001421972,0.0000107745,0.7914186,0.00002619008,0.2075573,0.0001538158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963201,0.0005021411,0.0003972363,0.0001569666,0.0001445713,0.0005882728,0.00013888,0.00001781159,0.001734033],"genre_scores_gemma":[0.9953614,0.0004637596,0.001267222,0.0003993009,0.0005170024,0.0003182418,0.0004390469,0.00003400812,0.001199989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2044523,"threshold_uncertainty_score":0.5436666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007744295322931792,"score_gpt":0.2275591613356486,"score_spread":0.2198148660127168,"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."}}