{"id":"W4387451683","doi":"10.12688/f1000research.140456.2","title":"Identification of high-performing antibodies for tyrosine-protein kinase SYK for use in Western Blot, immunoprecipitation and immunofluorescence","year":2023,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Neuroinflammation and Neurodegeneration Mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; Canadian Institutes of Health Research; Genentech; Government of Canada; Ontario Genomics Institute; Emory University; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Mitacs; Ontario Genomics; Genome Canada; Bristol-Myers Squibb; Bayer; Pfizer","keywords":"Syk; Immunoprecipitation; Tyrosine kinase; Biology; Western blot; Cancer research; Receptor tyrosine kinase; Molecular biology; Antibody; Cell biology; Kinase; Signal transduction; Immunology; Biochemistry; 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":[0.003059913,0.002957076,0.00129046,0.002324233,0.001303203,0.001120144,0.001365642,0.001268941,0.01194539],"category_scores_gemma":[0.002545793,0.001140485,0.001207812,0.001478842,0.0006287034,0.0009276908,0.0009149899,0.002591125,0.01101321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030555,"about_ca_system_score_gemma":0.0009499232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008631657,"about_ca_topic_score_gemma":0.002317024,"domain_scores_codex":[0.9974303,0.0005679639,0.0004590205,0.0004974843,0.0007282601,0.0003169772],"domain_scores_gemma":[0.9977987,0.0004871927,0.0001937623,0.0004984481,0.0008254298,0.0001964412],"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":[0.00007546899,0.00005877217,0.0002496111,0.0002570176,0.00002221424,0.0000881977,0.00004400074,0.00006422573,0.9901443,0.0004371742,0.001388928,0.007170128],"study_design_scores_gemma":[0.00006554449,0.0002410805,0.006287374,0.0001269978,0.0001461774,0.001199414,0.00005498149,0.001476011,0.9338996,0.0004730885,0.05598935,0.00004041466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1912509,0.01368136,0.7488714,0.001690277,0.001256513,0.003478387,0.0094224,0.005422837,0.02492581],"genre_scores_gemma":[0.1296166,0.008451308,0.8039057,0.0005871665,0.0002673747,0.004429786,0.02987229,0.001898467,0.02097137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01194539,"threshold_uncertainty_score":0.03996134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1249174995549248,"score_gpt":0.358125407367077,"score_spread":0.2332079078121522,"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."}}