{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003375651,0.0003054718,0.000649799,0.0005276634,0.0004050392,0.000229529,0.0005713335,0.000275201,0.000007933622],"category_scores_gemma":[0.001380797,0.0002247238,0.0002105063,0.0003222986,0.0006334122,0.0001692716,0.0004665187,0.0007989387,0.000009681883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001016283,"about_ca_system_score_gemma":0.0004706983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00296268,"about_ca_topic_score_gemma":0.0007648512,"domain_scores_codex":[0.9963442,0.0001906765,0.001136398,0.000668529,0.000931505,0.0007287058],"domain_scores_gemma":[0.9952834,0.002528302,0.0002454828,0.000697081,0.001128258,0.0001174566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02259212,0.0007170478,0.05972408,0.0304818,0.001181784,0.00003634766,0.005740745,0.0002985752,0.7993208,0.004188982,0.00175344,0.07396425],"study_design_scores_gemma":[0.006291464,0.001747706,0.6098464,0.005623001,0.0001992452,0.00002518343,0.001204852,0.02319732,0.3294447,0.01396936,0.00761059,0.0008402459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824196,0.00265445,0.001038502,0.004899552,0.0002211523,0.008014848,0.0006939864,0.00004716913,0.00001081936],"genre_scores_gemma":[0.9579357,0.00764972,0.003119571,0.00001872807,0.0002423586,0.004156969,0.0009088985,0.0001284631,0.02583963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5501223,"threshold_uncertainty_score":0.9163971,"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."}}