{"id":"W1831029901","doi":"10.1002/ijc.28632","title":"Intravital biobank and personalized cancer therapy: The correlation with omics","year":2013,"lang":"en","type":"article","venue":"International Journal of Cancer","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Guangxi Province","keywords":"Biobank; Omics; Cancer; Personalized medicine; Intravital microscopy; Medicine; Bioinformatics; Computational biology; Biology; Internal medicine","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":[0.006827129,0.0005434484,0.001306284,0.003091575,0.0008490248,0.004964136,0.0009400979,0.001887456,0.002660542],"category_scores_gemma":[0.008608151,0.0005365359,0.0006217779,0.003686975,0.001585611,0.00342774,0.002440924,0.002346317,0.0007960849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002196117,"about_ca_system_score_gemma":0.00200354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00230648,"about_ca_topic_score_gemma":0.002392825,"domain_scores_codex":[0.9965295,0.001961107,0.000159323,0.0006015225,0.0006128731,0.0001356742],"domain_scores_gemma":[0.9928527,0.003708424,0.001021861,0.00115992,0.0009492247,0.0003078257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001267862,0.0005223391,0.1136436,0.002585687,0.0009347617,0.001283413,0.001462137,0.02533313,0.1027983,0.1026834,0.02809903,0.6193864],"study_design_scores_gemma":[0.0001438432,0.0006766724,0.115801,0.001500177,0.001090246,0.005436379,0.003167089,0.1565086,0.1044209,0.3091187,0.3015801,0.0005562701],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08594528,0.075066,0.7855251,0.02843212,0.000918811,0.0004110408,0.005643864,0.002883673,0.0151741],"genre_scores_gemma":[0.5094001,0.03924042,0.434057,0.007692628,0.001074127,0.0005615609,0.003363028,0.0006064227,0.004004669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006827129,"threshold_uncertainty_score":0.03610575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005708686726405986,"score_gpt":0.2516596934366052,"score_spread":0.2459510067101992,"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."}}