{"id":"W2122441559","doi":"10.1039/c2an36296a","title":"Theoretical estimation of drag tag lengths for direct quantitative analysis of multiple miRNAs (DQAMmiR)","year":2012,"lang":"en","type":"article","venue":"The Analyst","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Drag; Oligonucleotide; microRNA; DNA; Computational biology; Biological system; Electrophoresis; Chemistry; RNA; Capillary electrophoresis; Drag coefficient; Chromatography; Biology; Gene; Biochemistry; Physics; Mechanics","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":[0.002341651,0.001146565,0.0006328697,0.001228685,0.0004281876,0.000831127,0.001128561,0.001068024,0.001001103],"category_scores_gemma":[0.005083945,0.0007705358,0.0006868904,0.0005684334,0.001180541,0.001841037,0.001016669,0.001246902,0.0008021509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001836912,"about_ca_system_score_gemma":0.0008258113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000807776,"about_ca_topic_score_gemma":0.0007320316,"domain_scores_codex":[0.9990063,0.0002111488,0.00003877228,0.0002713561,0.0003861738,0.000086129],"domain_scores_gemma":[0.9978079,0.001687602,0.0002163308,0.00008249554,0.0001561235,0.00004940901],"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":[0.0002860568,0.0001765391,0.003745254,0.001102613,0.00008277876,0.0007518179,0.0004853893,0.3601907,0.39524,0.154118,0.001229968,0.08259077],"study_design_scores_gemma":[0.00002034147,0.0001170433,0.0005796715,0.00005519237,0.00003129422,0.0002541261,0.00003992036,0.900859,0.07745558,0.0171892,0.003339091,0.00005958863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04525149,0.001665837,0.9477439,0.0002687868,0.00004124425,0.00008016999,0.00006655008,0.0004263975,0.004455694],"genre_scores_gemma":[0.5537619,0.00353983,0.4374315,0.0004666895,0.00005822505,0.0007382519,0.0001977888,0.0002585608,0.003547327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002341651,"threshold_uncertainty_score":0.01332778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468425479628361,"score_gpt":0.3208621521732265,"score_spread":0.3061778973769429,"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."}}