{"id":"W2771943208","doi":"10.1073/pnas.1714109114","title":"Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs","year":2017,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Carcinogens and Genotoxicity Assessment","field":"Biochemistry, Genetics and Molecular Biology","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Health and Environmental Sciences Institute; Sanofi; University of Arkansas for Medical Sciences; National Institute of Environmental Health Sciences; Teva Pharmaceutical Industries; Pfizer","keywords":"Computational biology; Biomarker; Transcriptome; Throughput; Biology; Data science; Computer science; Genetics; Gene; Gene expression","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":[0.0004378629,0.00007416334,0.0001171126,0.00008856187,0.0001955197,0.00002111106,0.0004678582,0.00008199449,0.00000403285],"category_scores_gemma":[0.00009347929,0.00005619177,0.0000381351,0.0001110226,0.0004219593,0.00001547549,0.000196976,0.00003681906,1.74238e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001301545,"about_ca_system_score_gemma":0.00004805833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001104302,"about_ca_topic_score_gemma":8.472792e-7,"domain_scores_codex":[0.999114,0.000004750456,0.0002332206,0.0001886098,0.0003511659,0.0001082749],"domain_scores_gemma":[0.9995015,0.000008335268,0.0002866604,0.00001717014,0.0001537415,0.00003258949],"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":[0.00002548496,0.00003152174,0.004821805,0.00003820434,0.00002313619,2.483686e-9,0.0001880934,0.00001916707,0.9906628,0.002226567,0.00008451407,0.001878676],"study_design_scores_gemma":[0.0001499078,0.00005224958,0.2683365,0.00002061982,0.000007940094,0.000003031109,0.0001336342,0.00001319866,0.7300331,0.0003434948,0.000856629,0.00004966091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980096,0.0001349247,0.000007050591,0.0009469492,0.00002818864,0.0001835196,0.0000191112,0.000001256432,0.000669451],"genre_scores_gemma":[0.9954447,0.00007972377,0.004265283,0.0001265699,0.00003272434,0.00001134448,5.606625e-7,0.000003039163,0.00003612095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2635147,"threshold_uncertainty_score":0.2291434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0402269089737811,"score_gpt":0.3114033982525357,"score_spread":0.2711764892787545,"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."}}