{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002886022,0.00006562725,0.0000496272,0.00007460337,0.0004908943,0.00003001526,0.0002083245,0.00004666073,0.000008786408],"category_scores_gemma":[0.00007126915,0.00006108981,0.0000393367,0.0004363244,0.0001561414,0.00000610032,0.0001630888,0.00017815,0.000001175527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003973999,"about_ca_system_score_gemma":0.0001409458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001748633,"about_ca_topic_score_gemma":0.000002601984,"domain_scores_codex":[0.9990225,0.00003027372,0.00009009624,0.0003215416,0.0004089681,0.0001265617],"domain_scores_gemma":[0.9996006,0.000006498552,0.0000497457,0.0001282307,0.0001763266,0.00003860635],"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":[0.00001300555,0.00003950082,0.0002445285,6.641826e-7,0.00000720683,0.000001670126,0.00001074935,0.0106306,0.978263,0.002380412,0.004044299,0.004364389],"study_design_scores_gemma":[0.0004322081,0.000470817,0.005432037,0.000003502271,0.00001977334,0.0001248472,0.00005931109,0.06948353,0.7859951,0.006631162,0.1307575,0.0005901899],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249079,0.000626682,0.06802076,0.004262212,0.0006095821,0.0002236362,0.00005570994,0.00008576692,0.001207773],"genre_scores_gemma":[0.980987,0.000004217263,0.01526521,0.003323678,0.0001413859,0.000006411207,0.0001828523,0.000004399274,0.0000848967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1922679,"threshold_uncertainty_score":0.3775612,"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."}}