{"id":"W4381432973","doi":"10.12688/f1000research.131852.2","title":"The identification of high-performing antibodies for TDP-43 for use in Western Blot, immunoprecipitation and immunofluorescence","year":2023,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Neurogenetic and Muscular Disorders Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Structural Genomics Consortium; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Genentech; Mitacs; Motor Neurone Disease Association; Government of Canada; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Bayer; ALS Society of Canada; Ontario Genomics; Genome Canada; Bristol-Myers Squibb; Pfizer; ALS Association","keywords":"Immunoprecipitation; Western blot; Immunofluorescence; Antibody; Biology; RNA; RNA splicing; Molecular biology; Blot; Transcription (linguistics); Virology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003468026,0.002841488,0.001312097,0.002554788,0.001496534,0.00129611,0.001346335,0.001410218,0.01159974],"category_scores_gemma":[0.003665766,0.001341711,0.001133269,0.00140198,0.0006907505,0.0009340437,0.001169641,0.002404725,0.01324876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008221873,"about_ca_system_score_gemma":0.0009078671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008162133,"about_ca_topic_score_gemma":0.002123157,"domain_scores_codex":[0.9971471,0.0006258101,0.000520963,0.0005692079,0.0007586881,0.0003781074],"domain_scores_gemma":[0.9972007,0.0006108192,0.0002044137,0.0007060136,0.001067843,0.0002101753],"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":[0.00009646922,0.00007109457,0.0002903001,0.0002775587,0.00002046989,0.0001154498,0.00008287941,0.00007769385,0.9888285,0.0005446344,0.002018772,0.007576146],"study_design_scores_gemma":[0.0000580324,0.0002652561,0.006379777,0.0001662004,0.000121815,0.001398212,0.00008424265,0.001596512,0.8948995,0.0006251889,0.09435803,0.00004720058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2001288,0.01174264,0.7334177,0.002195232,0.002063006,0.004151471,0.01103725,0.007106653,0.02815721],"genre_scores_gemma":[0.106659,0.006780262,0.8277139,0.0007963178,0.0003040056,0.006316013,0.02720999,0.002658041,0.02156243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01159974,"threshold_uncertainty_score":0.03880501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09262491739922563,"score_gpt":0.3954136839479826,"score_spread":0.302788766548757,"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."}}