{"id":"W2143316096","doi":"10.1002/anie.201006793","title":"Direct Genetic Analysis of Ten Cancer Cells: Tuning Sensor Structure and Molecular Probe Design for Efficient mRNA Capture","year":2011,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Nucleic acid; Cancer; Messenger RNA; Chip; Solubility; Nanotechnology; Cancer cell; Computational biology; Leukemia; Chemistry; Biology; Molecular biology; Materials science; Combinatorial chemistry; Cancer research; Biochemistry; Computer science; Genetics; Gene; Physical chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002530625,0.0003889478,0.0003181269,0.0001816121,0.0001417777,0.0003830328,0.0004605714,0.0004208531,0.0004776738],"category_scores_gemma":[0.0002506757,0.0002413612,0.0001904119,0.0001643578,0.0003648651,0.0002925252,0.0003098643,0.0006712225,0.000382554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000440659,"about_ca_system_score_gemma":0.0003113179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004196547,"about_ca_topic_score_gemma":0.001178262,"domain_scores_codex":[0.9998223,0.00001599476,0.000008257772,0.00005095578,0.00007693792,0.00002553492],"domain_scores_gemma":[0.9999014,0.00003289949,0.0000241259,0.000009295413,0.00001628466,0.00001601095],"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":[0.00001007175,0.000007601712,0.00003538873,0.00001748578,0.000001237301,0.000009256026,0.000006245172,0.0001297812,0.9980507,0.00007388635,0.00002944312,0.001629004],"study_design_scores_gemma":[0.000002777167,0.00003910796,0.0001305215,6.294941e-7,0.000002444091,0.00003071947,0.000003422522,0.001367483,0.997822,0.0000233354,0.0005744788,0.000003051681],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7406594,0.001677322,0.2535417,0.0003663132,0.00008964115,0.0002328478,0.0004083206,0.0009551296,0.00206933],"genre_scores_gemma":[0.7935221,0.001793234,0.1992574,0.0002030773,0.00002217506,0.0002844778,0.0005026472,0.00009934124,0.004315539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004776738,"threshold_uncertainty_score":0.003197193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402546884484004,"score_gpt":0.2608182361598395,"score_spread":0.2467927673149995,"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."}}