{"id":"W2412590065","doi":"10.1089/nat.2015.0573","title":"Fit for the Eye: Aptamers in Ocular Disorders","year":2016,"lang":"en","type":"review","venue":"Nucleic Acid Therapeutics","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genentech; Valeant Pharmaceuticals International","keywords":"Aptamer; Identification (biology); Selection (genetic algorithm); Computational biology; Computer science; Medicine; Biology; Artificial intelligence; Genetics; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004305589,0.0007946804,0.0008131361,0.001990201,0.0002866501,0.0008213119,0.0005351161,0.001241939,0.003555644],"category_scores_gemma":[0.0004931622,0.0002746172,0.0004593456,0.001379318,0.0004493941,0.001167383,0.0006941974,0.00229899,0.002711658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004752867,"about_ca_system_score_gemma":0.0005361207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000610923,"about_ca_topic_score_gemma":0.001198413,"domain_scores_codex":[0.9998282,0.0000222755,0.0000222827,0.00003318445,0.00007391877,0.00002015595],"domain_scores_gemma":[0.9998251,0.00008570717,0.00002527816,0.000005917676,0.0000395408,0.00001845403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003424257,0.00006274601,0.0001608918,0.005658497,0.00003827752,0.0002381596,0.00008000395,0.0002693449,0.003805134,0.004116101,0.02104925,0.9644873],"study_design_scores_gemma":[0.00000982747,0.00006604326,0.0004664902,0.001261522,0.00003589101,0.00205392,0.0000399282,0.00007680897,0.001041657,0.001643267,0.9932914,0.00001323442],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001970658,0.9967383,0.0003129654,0.0003245452,0.000285083,0.000007303507,0.00001631294,0.00001441557,0.002104103],"genre_scores_gemma":[0.001662332,0.9947954,0.0004911498,0.0004931049,0.0002374048,0.00001296586,0.00004245305,0.000004190134,0.002261118],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003555644,"threshold_uncertainty_score":0.01189488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03587853098624736,"score_gpt":0.3561565370747778,"score_spread":0.3202780060885305,"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."}}