{"id":"W2154722286","doi":"10.1093/bioinformatics/btr556","title":"miREnvironment Database: providing a bridge for microRNAs, environmental factors and phenotypes","year":2011,"lang":"en","type":"article","venue":"Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Phenotype; microRNA; Biology; Computational biology; Organism; Database; Bioinformatics; Genetics; Computer science; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002305683,0.002691513,0.002732659,0.007069152,0.001131768,0.002137865,0.002700629,0.001924424,0.02027341],"category_scores_gemma":[0.006752111,0.001205521,0.001610764,0.006646183,0.0003952395,0.002573434,0.003607644,0.001525595,0.02347623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000684203,"about_ca_system_score_gemma":0.002122737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001211324,"about_ca_topic_score_gemma":0.001785,"domain_scores_codex":[0.9983732,0.0002908005,0.0004316076,0.0004629427,0.0003511626,0.00009035406],"domain_scores_gemma":[0.9974996,0.0009684431,0.0005177864,0.0004149635,0.0003131629,0.0002859826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004160792,0.0005677668,0.01563509,0.02048871,0.0009654287,0.003656123,0.001252139,0.005733408,0.07550127,0.01955681,0.5745215,0.277961],"study_design_scores_gemma":[0.000440501,0.0001810944,0.01280671,0.0008887119,0.0003894411,0.001550388,0.000266622,0.01252015,0.02091823,0.00983215,0.9399897,0.0002163212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01142285,0.005075215,0.1438424,0.001114406,0.0004082079,0.0007247547,0.695899,0.1298494,0.0116637],"genre_scores_gemma":[0.01945345,0.001873864,0.1863423,0.0006210491,0.0001008957,0.001203416,0.7809332,0.006669401,0.002802428],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02027341,"threshold_uncertainty_score":0.06782132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372183527517547,"score_gpt":0.2219915743072171,"score_spread":0.1982697390320416,"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."}}