{"id":"W4281943668","doi":"10.1101/2022.06.03.494699","title":"The identification of potent and selective antibodies for Serine/threonine-protein kinase TBK1, for use in immunoblot, immunofluorescence and immunoprecipitation","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Amyotrophic Lateral Sclerosis Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; Structural Genomics Consortium","funders":"","keywords":"Immunoprecipitation; TANK-binding kinase 1; Immunofluorescence; Serine; Antibody; Threonine; Kinase; Phosphorylation; Amyotrophic lateral sclerosis; Molecular biology; Protein kinase A; Chemistry; Biology; Biochemistry; MAP kinase kinase kinase; Medicine; Immunology; 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":[0.00144123,0.001401134,0.0004694593,0.0007073801,0.0005696057,0.0005701255,0.0005340814,0.0006390282,0.002083746],"category_scores_gemma":[0.0007969581,0.0003876448,0.0005203427,0.0003439693,0.0003437749,0.0004672349,0.0003265033,0.001423196,0.001536074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006813899,"about_ca_system_score_gemma":0.0005390343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004298685,"about_ca_topic_score_gemma":0.001395333,"domain_scores_codex":[0.9994285,0.0001488527,0.00008601702,0.0001086461,0.0001204045,0.0001075492],"domain_scores_gemma":[0.9994208,0.0001605637,0.00007142155,0.0000831108,0.0001755435,0.0000887343],"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":[0.00004928738,0.00005995314,0.000202091,0.0001081874,0.00001300045,0.00004109892,0.00001957287,0.00007322699,0.9961275,0.0001759458,0.0002405128,0.002889673],"study_design_scores_gemma":[0.00003108365,0.0002038128,0.002487205,0.00002167753,0.00006287849,0.0004967682,0.00002045009,0.0006536162,0.985129,0.000106518,0.01077756,0.000009449866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7274298,0.02143373,0.2344635,0.001302168,0.0004624109,0.001309118,0.002966787,0.0006752954,0.009957127],"genre_scores_gemma":[0.6650341,0.01683684,0.2846234,0.001018981,0.0002819498,0.001471187,0.0164807,0.0004481844,0.01380476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002083746,"threshold_uncertainty_score":0.007622004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235599336738988,"score_gpt":0.2703444459935347,"score_spread":0.2479884526261448,"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."}}