{"id":"W2141646155","doi":"10.1093/nar/gkm156","title":"An in vitro selection scheme for oligonucleotide probes to discriminate between closely related DNA sequences","year":2007,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôtel-Dieu de Québec; Centre Hospitalier Universitaire Sainte-Justine","funders":"Canadian Institutes of Health Research","keywords":"Biology; Oligonucleotide; Computational biology; Nucleic acid; Nucleic acid thermodynamics; Selection (genetic algorithm); Complementary sequences; Molecular probe; DNA; Hybridization probe; Directed Molecular Evolution; DNA sequencing; Oligomer restriction; Human papillomavirus; Genetics; Base sequence; Gene; Computer science; Artificial intelligence; Directed evolution","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":[0.001066969,0.001634018,0.001049991,0.0005766038,0.0004364026,0.0006682278,0.0008516472,0.0009187138,0.001737482],"category_scores_gemma":[0.001293746,0.0008758302,0.0008686789,0.0003941873,0.0005309871,0.0003565809,0.0007902989,0.002172537,0.001716585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003106466,"about_ca_system_score_gemma":0.0002978207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002733936,"about_ca_topic_score_gemma":0.0007506998,"domain_scores_codex":[0.9982115,0.0007991995,0.0001454779,0.0003574752,0.0003518893,0.0001344224],"domain_scores_gemma":[0.9987372,0.0005093949,0.0002191923,0.0002715625,0.0001638899,0.00009871823],"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":[0.00002623886,0.00001840708,0.00004142955,0.00003173792,0.000008200826,0.0000203182,0.00001632948,0.0000638939,0.9981787,0.00009306886,0.0000518366,0.001449717],"study_design_scores_gemma":[0.00001139823,0.0002320963,0.0003062994,0.000005720074,0.00004414707,0.000312788,0.000007868306,0.0008202001,0.994754,0.00004491453,0.003445263,0.00001529825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2309524,0.002339793,0.7588549,0.0004414963,0.0004351912,0.00126679,0.0007040236,0.002038045,0.002967422],"genre_scores_gemma":[0.4073024,0.003669891,0.5724344,0.0007143542,0.0001604474,0.002229939,0.003024472,0.0003740384,0.0100901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001737482,"threshold_uncertainty_score":0.005812407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0399139023228248,"score_gpt":0.3927172146283254,"score_spread":0.3528033123055007,"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."}}