{"id":"W3062204540","doi":"10.26685/urncst.188","title":"Using CRISPR-X for Optimization of Antibodies Towards A30P α-synuclein Oligomers in Immunotherapy of Parkinson’s Disease","year":2020,"lang":"en","type":"article","venue":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"CRISPR; Parkinson's disease; Immunotherapy; Antibody; Disease; Computational biology; Virology; Medicine; Biology; Immunology; Genetics; Immune system; Gene; Pathology","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":[0.0003091879,0.0005971037,0.0003685062,0.0002664913,0.0001470248,0.0006301603,0.0003606217,0.0005466513,0.00147166],"category_scores_gemma":[0.0002034095,0.0001740934,0.0003768476,0.0001938339,0.0001778318,0.0001848274,0.0003169526,0.0005247223,0.0006029035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003517646,"about_ca_system_score_gemma":0.0001964911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000365866,"about_ca_topic_score_gemma":0.000552494,"domain_scores_codex":[0.9997469,0.00003764474,0.00003055879,0.00005802845,0.00008501271,0.00004197944],"domain_scores_gemma":[0.9998969,0.0000233566,0.00003759136,0.000008854129,0.00001428185,0.00001907743],"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":[0.00007342599,0.000081369,0.0002256705,0.00009355017,0.00001826201,0.00007556707,0.00001978179,0.001597236,0.9925048,0.0002025986,0.0001305723,0.004977121],"study_design_scores_gemma":[0.0000364695,0.0006762685,0.001191041,0.00001683232,0.00004030489,0.0002512942,0.00001996558,0.004838077,0.9855409,0.0000931148,0.007280726,0.00001504857],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9200728,0.003435658,0.0672122,0.0002947722,0.0001207681,0.0003990255,0.001042761,0.0008779206,0.006544161],"genre_scores_gemma":[0.9403562,0.001744068,0.05078762,0.0001662726,0.00001893002,0.000278602,0.001086851,0.0001260567,0.00543548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00147166,"threshold_uncertainty_score":0.004923224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06278434175943126,"score_gpt":0.4418306075762427,"score_spread":0.3790462658168114,"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."}}