{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007741899,0.00006554482,0.00006472252,0.0000212293,0.00002996897,0.00005973118,0.0001492814,0.00004316643,0.00009527435],"category_scores_gemma":[0.000004838825,0.00003692479,0.00003576955,0.0000207164,0.0000811656,0.00001309664,0.00002371498,0.00008812221,0.000001073304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002132147,"about_ca_system_score_gemma":0.00008689444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008579734,"about_ca_topic_score_gemma":0.00004405458,"domain_scores_codex":[0.9995653,0.0000110631,0.0001561425,0.00005309892,0.0001450194,0.00006936416],"domain_scores_gemma":[0.9994125,0.000008982731,0.0001946183,0.000048273,0.0003030417,0.00003254095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002037991,0.0001043264,0.08179083,0.00001394417,0.003111893,0.000007941562,0.001997849,0.00590722,0.1079564,0.001867377,0.03431112,0.760893],"study_design_scores_gemma":[0.01654762,0.001805852,0.2249445,0.0004050409,0.0002790458,0.0009583868,0.001912599,0.01834329,0.03123948,0.006225538,0.6961785,0.001160106],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825163,0.008905618,0.003917279,0.003669779,0.0006574893,0.000100389,0.00001512347,0.000001342841,0.0002166743],"genre_scores_gemma":[0.99211,0.005630238,0.0002467327,0.001074082,0.0006900949,0.00001094489,0.00000655613,0.00000743995,0.0002238719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.759733,"threshold_uncertainty_score":0.1505749,"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."}}