{"id":"W4328095950","doi":"10.12688/f1000research.131333.1","title":"The identification of high-performing antibodies for transmembrane protein 106B (TMEM106B) for use in Western blot, immunoprecipitation, and immunofluorescence","year":2023,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University; Structural Genomics Consortium; Montreal Neurological Institute and Hospital","funders":"Genentech; Canadian Institutes of Health Research; Innovative Medicines Initiative; 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; Identification (biology); Transmembrane protein; Biology; Immunology; Biochemistry; Botany","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":[0.00315094,0.001875247,0.0008535075,0.001838525,0.001017006,0.0009907637,0.001161922,0.001367341,0.004650521],"category_scores_gemma":[0.002930062,0.0009348868,0.0009851534,0.001044971,0.0005476105,0.0007961896,0.0008334543,0.002063308,0.005954102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006936931,"about_ca_system_score_gemma":0.0005856122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006267638,"about_ca_topic_score_gemma":0.001459169,"domain_scores_codex":[0.9979844,0.0004652052,0.0003658571,0.0003630058,0.0006053543,0.0002162432],"domain_scores_gemma":[0.9980348,0.0003501103,0.0002558992,0.0004347099,0.0007228746,0.0002016308],"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":[0.00004842011,0.00002731476,0.0002285888,0.0001302453,0.00001109126,0.00004759029,0.00003864914,0.0000273755,0.9953125,0.0001561428,0.0005548447,0.00341731],"study_design_scores_gemma":[0.00003322675,0.0001708032,0.005462648,0.0000737134,0.00006978484,0.0009507241,0.00004040394,0.0006178101,0.9692514,0.0001714597,0.02313709,0.00002082083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4268306,0.01397727,0.5250261,0.002270318,0.001103884,0.00226099,0.00883671,0.003642414,0.01605163],"genre_scores_gemma":[0.2244438,0.007827486,0.7206547,0.0009386812,0.0002067578,0.002677819,0.0266237,0.001639733,0.0149873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004650521,"threshold_uncertainty_score":0.01666397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08514597391120586,"score_gpt":0.3795435631763445,"score_spread":0.2943975892651386,"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."}}