{"id":"W188094435","doi":"","title":"Quantitative analysis of p53 expression in human normal and cancer tissue microarray with global normalization method.","year":2011,"lang":"en","type":"article","venue":"PubMed","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Normalization (sociology); Tissue microarray; Immunohistochemistry; Microarray; Microarray analysis techniques; Pathology; Biology; Proteomics; Stain; Staining; Computational biology; Gene expression; Medicine; Gene; Genetics","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":[0.003226854,0.001251243,0.001103267,0.002476708,0.0006123771,0.0007772485,0.0008525277,0.0006305294,0.002500144],"category_scores_gemma":[0.001945313,0.0005523407,0.0008915156,0.002885819,0.000892142,0.0007515169,0.0006697215,0.001119253,0.001103083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008659087,"about_ca_system_score_gemma":0.0005393293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006499423,"about_ca_topic_score_gemma":0.001527313,"domain_scores_codex":[0.9963462,0.0008621394,0.0002274073,0.001126499,0.001249233,0.0001885144],"domain_scores_gemma":[0.9990132,0.0002762399,0.0001585772,0.0001967752,0.0003160224,0.00003908753],"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":[0.0001957152,0.00005423951,0.00143795,0.0003110668,0.00005573787,0.00005231796,0.00009686995,0.0004317562,0.9761112,0.0004005503,0.0004051744,0.02044741],"study_design_scores_gemma":[0.00002301378,0.0004533052,0.02076501,0.00003088595,0.0001666532,0.0007141514,0.00007421822,0.006968331,0.9601109,0.0006026077,0.0100366,0.00005436065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2046578,0.006613404,0.7755653,0.0002831972,0.0003712183,0.0007722178,0.002673361,0.003461586,0.005601916],"genre_scores_gemma":[0.3495775,0.003908677,0.6234965,0.0003187709,0.0001003643,0.004050967,0.007277384,0.0007238284,0.01054612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003226854,"threshold_uncertainty_score":0.01706541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02170765737018578,"score_gpt":0.3125139352143068,"score_spread":0.290806277844121,"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."}}