{"id":"W2896113647","doi":"10.1002/em.22243","title":"Using a gene expression biomarker to identify DNA damage‐inducing agents in microarray profiles","year":2018,"lang":"en","type":"article","venue":"Environmental and Molecular Mutagenesis","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"U.S. Environmental Protection Agency","keywords":"Biomarker; Toxicogenomics; Biology; False positive paradox; Computational biology; Gene; Context (archaeology); Microarray; DNA damage; Transcriptome; DNA microarray; Gene expression; Genetics; DNA; Computer science; Machine learning","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":[0.0001433781,0.0002177208,0.0001440808,0.00009650946,0.0001401647,0.00002455798,0.0001799692,0.0001785438,0.00005202799],"category_scores_gemma":[0.00001003754,0.000219614,0.00006810394,0.00008952899,0.0001578032,0.000005745074,0.0003844194,0.00005433597,0.00002308707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003005356,"about_ca_system_score_gemma":0.00001321277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002528185,"about_ca_topic_score_gemma":0.000005990217,"domain_scores_codex":[0.9986498,0.00008841205,0.000219191,0.0006428346,0.0001069386,0.0002928115],"domain_scores_gemma":[0.9993935,0.000002664407,0.00006106064,0.000405546,0.000007541599,0.0001297435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002983339,0.00007181903,0.005035261,0.000005373382,0.00002007957,0.00001791502,0.00004556377,0.000006652061,0.9935805,0.000004169444,0.0001470602,0.001035731],"study_design_scores_gemma":[0.0002523193,0.0001001359,0.03855029,0.00001841801,0.00001710528,0.0000280697,0.00004947414,0.00004695802,0.9568586,0.0000198471,0.00380303,0.0002557601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814303,0.0002996972,0.01755958,0.00006874518,0.0000413671,0.0004404215,0.00003851367,0.00001373671,0.0001076634],"genre_scores_gemma":[0.9810288,0.00006781019,0.01777302,0.0006898631,0.00005783352,0.00008826787,0.0001649438,0.00003896306,0.00009052308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03672195,"threshold_uncertainty_score":0.8955598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02280551860648428,"score_gpt":0.3092974983974899,"score_spread":0.2864919797910057,"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."}}