{"id":"W2999018780","doi":"10.3390/biomedicines8010010","title":"Meta-Analysis of Hypoxic Transcriptomes from Public Databases","year":2020,"lang":"en","type":"article","venue":"Biomedicines","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Bioscience Database Center; Institute of Genetics; Japan Science and Technology Agency; Research Organization of Information and Systems","keywords":"Transcriptome; Biology; Hypoxia (environmental); Transcription factor; Gene; Gene expression; Database; Hypoxia-inducible factors; Gene expression profiling; Computational biology; Bioinformatics; Genetics; Oxygen; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008990661,0.002189695,0.004059842,0.01588359,0.001204597,0.0033571,0.002623968,0.001174762,0.001532647],"category_scores_gemma":[0.01402258,0.0009553864,0.00647694,0.01365687,0.0005233834,0.001630422,0.002364702,0.001320329,0.0005083428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001629063,"about_ca_system_score_gemma":0.002564393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003556039,"about_ca_topic_score_gemma":0.006053303,"domain_scores_codex":[0.9930897,0.002284655,0.000815461,0.00233866,0.00116185,0.00030965],"domain_scores_gemma":[0.9907142,0.005360865,0.001043048,0.001732131,0.0008789725,0.0002707804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.007818921,0.001384664,0.2126836,0.04552982,0.1033101,0.003765524,0.001966551,0.1124693,0.2807252,0.01000836,0.02236375,0.1979741],"study_design_scores_gemma":[0.0008457912,0.001896288,0.3370318,0.003859715,0.07196971,0.003401362,0.003746689,0.2545893,0.1285582,0.04728664,0.1458363,0.0009783024],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3467017,0.03860543,0.1843091,0.001243294,0.0004674742,0.001081344,0.4121008,0.011124,0.00436695],"genre_scores_gemma":[0.373558,0.007510148,0.1731389,0.0006104854,0.0001494993,0.00196523,0.4414151,0.001082947,0.0005697936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01588359,"threshold_uncertainty_score":0.0475477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.146524091477059,"score_gpt":0.3060610260646263,"score_spread":0.1595369345875673,"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."}}