{"id":"W3102440231","doi":"10.1101/455345","title":"<i>RadiationGeneSigDB</i> : A database of oxic and hypoxic radiation response gene signatures and their utility in identification of hypoxia-regulated MicroRNA","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut universitaire de cardiologie et de pneumologie de Québec","funders":"","keywords":"Computational biology; Gene; Radiation therapy; Gene expression; Transcriptome; Gene signature; Biology; microRNA; Hypoxia (environmental); Bioinformatics; Database; Medicine; Computer science; Internal medicine; 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":[0.0009163726,0.0009778119,0.001021775,0.005081488,0.0004527373,0.001203285,0.001067097,0.0008032538,0.007168475],"category_scores_gemma":[0.003024333,0.0002864551,0.0008177377,0.005896218,0.0003458185,0.0006600696,0.001210832,0.0006164822,0.004504143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006697959,"about_ca_system_score_gemma":0.001324817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00411093,"about_ca_topic_score_gemma":0.005957354,"domain_scores_codex":[0.9990375,0.0001164163,0.0001740118,0.0003139882,0.0002627278,0.0000953398],"domain_scores_gemma":[0.9980054,0.0006570282,0.0004993778,0.0003276608,0.0002987668,0.0002118129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003992616,0.0003497795,0.07667423,0.01656631,0.001185408,0.003618025,0.001222694,0.0122961,0.2262343,0.007577079,0.4958141,0.1544694],"study_design_scores_gemma":[0.0003955011,0.0004005698,0.140768,0.0009047725,0.0005423753,0.002759878,0.0004641142,0.01273294,0.06998874,0.006343082,0.7644508,0.0002492096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05131949,0.002702012,0.008061976,0.0003199295,0.0001064406,0.0001647168,0.921297,0.01139774,0.004630692],"genre_scores_gemma":[0.03868099,0.0008756547,0.01736498,0.0002080957,0.00003871843,0.0002863921,0.9409518,0.0008343986,0.0007589103],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007168475,"threshold_uncertainty_score":0.02398092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009916433185762418,"score_gpt":0.2325709999186676,"score_spread":0.2226545667329052,"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."}}