{"id":"W4309082069","doi":"10.1007/978-1-0716-2744-0_2","title":"Optimal CCN4 Immunofluorescence for Tissue Microarray","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Connective Tissue Growth Factor Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Queen's University","funders":"Canadian Institutes of Health Research; National Institutes of Health","keywords":"Immunofluorescence; Tissue microarray; Biology; Microarray; Microarray analysis techniques; Cell biology; Epithelial–mesenchymal transition; Cancer research; Mesenchymal stem cell; Computational biology; Gene expression; Gene; Antibody; Transition (genetics); Immunology; Immunohistochemistry; Genetics","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":[0.001811387,0.001591758,0.001038296,0.001825979,0.001543903,0.001157489,0.001600036,0.001182796,0.01457659],"category_scores_gemma":[0.001509996,0.002121969,0.0009242257,0.001401145,0.0009381424,0.001062454,0.001243605,0.002325008,0.0057175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002000035,"about_ca_system_score_gemma":0.001826511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002897295,"about_ca_topic_score_gemma":0.01203204,"domain_scores_codex":[0.9975982,0.0004436436,0.0002123362,0.000683603,0.0005199105,0.000542344],"domain_scores_gemma":[0.9987974,0.0003747364,0.0000938899,0.0003773781,0.0002563198,0.0001002143],"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":[0.00009570745,0.0000440362,0.0001610295,0.0001343814,0.00001308505,0.00002888019,0.00004247054,0.0002995372,0.9918235,0.0013241,0.0008231633,0.005210213],"study_design_scores_gemma":[0.00004806669,0.0001310273,0.002556339,0.00005233078,0.00003925627,0.0002440877,0.00004075603,0.007438601,0.9603889,0.001393236,0.02762493,0.00004242664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1675409,0.002785088,0.7907706,0.001315413,0.0005159865,0.001897909,0.004688863,0.007498912,0.02298627],"genre_scores_gemma":[0.1326209,0.002325204,0.8356619,0.0006237245,0.000152815,0.004485825,0.006941328,0.002012165,0.01517606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01457659,"threshold_uncertainty_score":0.04876351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723571618061696,"score_gpt":0.4223830674845351,"score_spread":0.3951473513039181,"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."}}