{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006525298,0.0006098706,0.0003521796,0.0005041711,0.0001470476,0.000799951,0.0007100438,0.00155258,0.00300158],"category_scores_gemma":[0.0005658357,0.000349066,0.000361386,0.0004515725,0.0002619684,0.0008022345,0.0004253518,0.0009066319,0.002995736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004652241,"about_ca_system_score_gemma":0.0002347924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002963439,"about_ca_topic_score_gemma":0.0003225057,"domain_scores_codex":[0.9993849,0.0001403524,0.00002941429,0.0001018631,0.0003030571,0.00004041759],"domain_scores_gemma":[0.9997576,0.00009559678,0.00003546434,0.00001354222,0.00008400412,0.00001378242],"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":[0.0001006633,0.00005701312,0.0002614672,0.001344252,0.00003886178,0.0001942354,0.00005531591,0.00089527,0.882647,0.005970323,0.005706375,0.1027292],"study_design_scores_gemma":[0.00001511526,0.0002533403,0.0005619599,0.0001416283,0.00003761541,0.0007382112,0.00004365745,0.006443329,0.8323773,0.00205922,0.1572861,0.00004254238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06847423,0.2493127,0.6182665,0.004132966,0.002659301,0.0004571721,0.001210217,0.00288472,0.05260223],"genre_scores_gemma":[0.4248292,0.1011854,0.425132,0.003280414,0.0008192026,0.0005810873,0.001260708,0.0002116664,0.04270024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00300158,"threshold_uncertainty_score":0.01004124,"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."}}