{"id":"W2263543537","doi":"10.1038/srep15552","title":"Screening and Identifying a Novel ssDNA Aptamer against Alpha-fetoprotein Using CE-SELEX","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China; Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences; Chinese Academy of Sciences; National Science Foundation","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Alpha (finance); Computational biology; Biology; Bioinformatics; Genetics; Medicine; RNA; Gene","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":[0.0003981763,0.0005928495,0.000595537,0.0003558979,0.0002050129,0.0005294058,0.0003863679,0.0004446026,0.0008042637],"category_scores_gemma":[0.0006045823,0.00020127,0.0003862961,0.0002969073,0.0002553015,0.0003390273,0.0004320903,0.0003583015,0.0004744647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002097963,"about_ca_system_score_gemma":0.0002532945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002820981,"about_ca_topic_score_gemma":0.0004373789,"domain_scores_codex":[0.999747,0.00004390163,0.00002575643,0.00006028,0.00009283955,0.00003020064],"domain_scores_gemma":[0.9998412,0.00006108678,0.00002146588,0.00001382426,0.00003998184,0.00002251328],"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":[0.00008415782,0.00006919967,0.000462297,0.00008915572,0.00002262297,0.0001659041,0.00005546305,0.00148037,0.9891735,0.0001879269,0.00008123723,0.008128009],"study_design_scores_gemma":[0.00001031055,0.0001283499,0.0003306379,0.000004108009,0.00001639739,0.0001850433,0.00002729389,0.005585446,0.992474,0.00006006509,0.001169038,0.000009282613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878024,0.001046445,0.1071516,0.0001737344,0.00003460818,0.0004704552,0.0003886101,0.0003386978,0.002593383],"genre_scores_gemma":[0.8436289,0.001344213,0.143945,0.0002163485,0.000009104258,0.0004116369,0.001314198,0.0001190772,0.009011413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008042637,"threshold_uncertainty_score":0.002690554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05115850328491166,"score_gpt":0.3107271589146767,"score_spread":0.259568655629765,"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."}}