{"id":"W2974773440","doi":"10.1259/bjr.20190198","title":"<i>RadiationGeneSigDB</i> : a database of oxic and hypoxic radiation response gene signatures and their utility in pre-clinical research","year":2019,"lang":"en","type":"article","venue":"British Journal of Radiology","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut universitaire de cardiologie et de pneumologie de Québec","funders":"","keywords":"Gene; Computational biology; Radiation therapy; Gene signature; Gene expression; Breast cancer; Hypoxia (environmental); Bioinformatics; Database; Medicine; Biology; Cancer; Computer science; Genetics; 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":[0.001541392,0.001351802,0.001369811,0.006273275,0.0004820149,0.001726861,0.001506008,0.001109427,0.01180272],"category_scores_gemma":[0.005555288,0.0004316208,0.0008737709,0.007108322,0.0004133917,0.0008898239,0.001624311,0.000825569,0.007225631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007700825,"about_ca_system_score_gemma":0.002063278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004815505,"about_ca_topic_score_gemma":0.006524697,"domain_scores_codex":[0.9986488,0.0002048584,0.0002523579,0.000438214,0.0003289811,0.00012682],"domain_scores_gemma":[0.9967721,0.001051675,0.0008496479,0.0005730963,0.0004115123,0.0003420007],"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":[0.005820835,0.000510296,0.09599958,0.01956228,0.00160632,0.002863352,0.001114307,0.01234919,0.09879909,0.007126821,0.553641,0.2006069],"study_design_scores_gemma":[0.0008097396,0.0006709556,0.1521624,0.001373911,0.0007484697,0.002364749,0.0004315926,0.01143961,0.03410868,0.007786693,0.7878056,0.0002975626],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01928725,0.001868026,0.007305989,0.0002925682,0.00007145766,0.0001744702,0.9579317,0.01034243,0.002726028],"genre_scores_gemma":[0.02881082,0.0009763258,0.01656862,0.0002726461,0.00004863212,0.0003857597,0.9513712,0.0009016299,0.000664368],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01180272,"threshold_uncertainty_score":0.03948408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225445147722928,"score_gpt":0.3196767295608785,"score_spread":0.2971322147885857,"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."}}