{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009427908,0.0001211888,0.0001132745,0.0000765285,0.00015248,0.00003093804,0.0009209513,0.0001403904,0.0002120031],"category_scores_gemma":[0.00008160913,0.0001112236,0.00001391545,0.0003177074,0.0006338978,0.00001170227,0.001671644,0.0000862571,0.00009420184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004908389,"about_ca_system_score_gemma":0.00003831568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000212917,"about_ca_topic_score_gemma":0.000007503626,"domain_scores_codex":[0.9986147,0.00001492696,0.0001391432,0.0008617117,0.0001391952,0.0002302662],"domain_scores_gemma":[0.9991469,0.000003870741,0.00005213606,0.0006138072,0.0000051098,0.0001781204],"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":[0.00001283536,0.00003436522,0.008325308,5.078805e-7,0.0000053233,0.00000188818,0.00003789916,0.00001819289,0.9875923,0.00001834932,0.0009550872,0.002997918],"study_design_scores_gemma":[0.0001822916,0.0001917317,0.03893089,0.000001216286,0.000007848472,0.00000623803,0.0005205651,0.0002445976,0.8841943,0.00005567292,0.07550235,0.0001623304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942315,0.0002747518,0.001346446,0.003627596,0.00005864458,0.0001497623,0.0001503935,0.00002657015,0.0001343506],"genre_scores_gemma":[0.9946193,0.0003491454,0.003508423,0.001228669,0.00007304693,0.00001023589,0.0001367574,0.000009391938,0.00006501444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1033981,"threshold_uncertainty_score":0.4535567,"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."}}