{"id":"W1816107362","doi":"10.1111/j.1747-0285.2011.01125.x","title":"Entropic Fragment‐Based Approach to Aptamer Design","year":2011,"lang":"en","type":"article","venue":"Chemical Biology & Drug Design","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Computational biology; DNA; RNA; Template; Binding affinities; Exponential growth; Molecular recognition; Biology; Chemistry; Combinatorial chemistry; Nanotechnology; Computer science; Biochemistry; Genetics; Gene; Materials science; Mathematics; Molecule","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000306989,0.0003111525,0.0003119144,0.00008386278,0.00007176662,0.00001145966,0.000401462,0.0003412585,0.00001244767],"category_scores_gemma":[0.0001295265,0.0002534239,0.0001638795,0.0001952093,0.000259446,0.000003166206,0.0001177331,0.0001581291,0.00002937376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000287393,"about_ca_system_score_gemma":0.00005762681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008099319,"about_ca_topic_score_gemma":2.598642e-7,"domain_scores_codex":[0.9981338,0.0002300557,0.0002962165,0.00077164,0.00009185082,0.000476473],"domain_scores_gemma":[0.9990178,0.00004049558,0.00009657479,0.0005504081,0.00009733409,0.0001974019],"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":[0.0002350186,0.0002200859,0.0001671862,0.000005089715,0.00007279551,0.00000187165,0.00003215145,0.0000156527,0.9919074,0.00007600346,0.004872012,0.002394719],"study_design_scores_gemma":[0.0002988901,0.0002195851,0.00002430296,0.000007706913,0.00005133227,0.000006047484,0.00002214145,0.0002424699,0.9938443,0.0003568143,0.004581982,0.0003444612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06644414,0.0002386154,0.9309999,0.0001187475,0.00006437832,0.0004776407,0.00001086852,0.0001280608,0.001517614],"genre_scores_gemma":[0.7001625,0.00002413218,0.2982588,0.001107526,0.0001091035,0.0000611547,0.0000967946,0.00002355722,0.0001563688],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6337184,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03464690042329682,"score_gpt":0.2600859243408712,"score_spread":0.2254390239175744,"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."}}