{"id":"W4281892973","doi":"10.1038/s43588-022-00253-w","title":"AI-powered aptamer generation","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; University of Alberta","funders":"","keywords":"Aptamer; Identification (biology); Computer science; Task (project management); Artificial intelligence; Computational biology; Engineering; Biology; Systems engineering; Genetics","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":[0.0001663362,0.0003005327,0.0002360273,0.0002447825,0.0003421936,0.0005882981,0.0006285192,0.0006328712,0.008068302],"category_scores_gemma":[0.0004271709,0.0001724699,0.0002027867,0.0002807991,0.0002797919,0.0005771481,0.0007280743,0.0007798155,0.002058955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004152661,"about_ca_system_score_gemma":0.0001983801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000179337,"about_ca_topic_score_gemma":0.000283853,"domain_scores_codex":[0.9998556,0.00001059845,0.000005108405,0.0000315156,0.00006479736,0.00003220785],"domain_scores_gemma":[0.9998704,0.00004333418,0.00001509518,0.00002662025,0.00002810013,0.00001638172],"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":[0.0002481414,0.000114896,0.000463123,0.0003424573,0.00003212066,0.0002631392,0.0001446442,0.0124494,0.8565396,0.03326431,0.004651248,0.09148689],"study_design_scores_gemma":[0.00005498586,0.0001579253,0.0004231067,0.0000142664,0.00002353081,0.0003029153,0.00003497194,0.1181983,0.8341962,0.008737157,0.03782503,0.00003162705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.41011,0.002589179,0.3830067,0.002042963,0.001814127,0.0004370006,0.0008764264,0.004692087,0.1944316],"genre_scores_gemma":[0.9325588,0.0004932137,0.04226297,0.0004261423,0.00007179009,0.0001473436,0.0002387546,0.0001642063,0.02363677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008068302,"threshold_uncertainty_score":0.02699113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007334456239723464,"score_gpt":0.302409602229916,"score_spread":0.2950751459901926,"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."}}