{"id":"W3120668689","doi":"10.1089/ten.tec.2020.0300","title":"Applications of Omics Technologies for Three-Dimensional <i>In Vitro</i> Disease Models","year":2021,"lang":"en","type":"review","venue":"Tissue Engineering Part C Methods","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Omics; Disease; Metabolomics; Leverage (statistics); Epigenomics; Proteomics; Computational biology; Systems biology; Emerging technologies; Genomics; Data science; Biology; Bioinformatics; Computer science; Medicine; Artificial intelligence; Genome; Pathology; DNA methylation; 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":[0.005436588,0.001412169,0.001982238,0.003823063,0.0008606189,0.005444587,0.001483945,0.002108474,0.004725899],"category_scores_gemma":[0.004853148,0.0006355273,0.002344333,0.00340622,0.001489378,0.004405307,0.002818151,0.003462028,0.002774741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001761438,"about_ca_system_score_gemma":0.002389599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001982825,"about_ca_topic_score_gemma":0.002373607,"domain_scores_codex":[0.9966782,0.001014064,0.0003188959,0.0004679429,0.001329319,0.0001915788],"domain_scores_gemma":[0.995474,0.0020331,0.0004953607,0.0005321752,0.001275833,0.0001895104],"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":[0.0004944783,0.000160649,0.007705059,0.02497518,0.0009955931,0.001830237,0.001082883,0.008550015,0.2576748,0.1327895,0.06701243,0.4967292],"study_design_scores_gemma":[0.00002776903,0.0002044388,0.004718211,0.00307747,0.0004605703,0.001538469,0.0007215758,0.009170724,0.1029329,0.04790065,0.8289829,0.0002644172],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01791391,0.3233054,0.5689468,0.01920407,0.004656348,0.0008026381,0.01418483,0.004632015,0.04635399],"genre_scores_gemma":[0.09285048,0.4270676,0.4424674,0.0100919,0.002385826,0.00144499,0.01502298,0.0009173081,0.007751529],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005444587,"threshold_uncertainty_score":0.02875173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03739352309677238,"score_gpt":0.3628080380771284,"score_spread":0.325414514980356,"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."}}