{"id":"W3006744920","doi":"10.1021/acs.est.9b06607","title":"EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species","year":2020,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Environment and Climate Change Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment Canada; Shell United States; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Genome Prairie; U.S. Environmental Protection Agency; McGill University; Génome Québec; U.S. Army Corps of Engineers; Genome Canada; University of Saskatchewan","keywords":"Toxicogenomics; Pimephales promelas; KEGG; Computational biology; Minnow; Ecotoxicology; Set (abstract data type); Microarray analysis techniques; Data mining; Biology; Gene; Computer science; Ecology; Gene expression; Genetics; Transcriptome; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001304576,0.001205933,0.0009166311,0.002966673,0.0006638814,0.001182289,0.0009726602,0.0005376783,0.00336505],"category_scores_gemma":[0.002355823,0.0006136904,0.001673452,0.001885791,0.0006339179,0.0009851909,0.001710371,0.0009293442,0.001080406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007273576,"about_ca_system_score_gemma":0.0009815076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001356841,"about_ca_topic_score_gemma":0.003404474,"domain_scores_codex":[0.9991272,0.0001044354,0.0001220866,0.0003712604,0.0002070113,0.00006802352],"domain_scores_gemma":[0.9986357,0.0004963088,0.0003011514,0.0002848937,0.0001734446,0.0001084615],"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":[0.002735005,0.0005020645,0.06528019,0.003455379,0.001188113,0.001289588,0.001904064,0.02674765,0.6418145,0.01461625,0.02859719,0.21187],"study_design_scores_gemma":[0.0003675832,0.001561425,0.172351,0.0005112687,0.0007114423,0.002101409,0.001111027,0.1241892,0.437828,0.03818446,0.2202743,0.0008088952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.246663,0.001304986,0.5501351,0.0004632995,0.0003581297,0.001073515,0.1427113,0.05179877,0.005491907],"genre_scores_gemma":[0.2007817,0.0008959175,0.646267,0.0006311044,0.00007914202,0.004310645,0.1370281,0.006690579,0.003315782],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00336505,"threshold_uncertainty_score":0.01125717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181872277186677,"score_gpt":0.2432722657490049,"score_spread":0.2214535429771382,"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."}}