{"id":"W2884335298","doi":"10.1093/bioinformatics/bty655","title":"The epiGenomic Efficient Correlator (epiGeEC) tool allows fast comparison of user datasets with thousands of public epigenomic datasets","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Canarie","keywords":"Epigenomics; Computer science; Epigenome; Source code; Variety (cybernetics); R package; Data mining; Biology; Artificial intelligence; DNA methylation; Computational science; Genetics","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":[0.000744607,0.0002205488,0.0002891925,0.00006711002,0.0002386793,0.00005402101,0.0005198713,0.0001486113,0.0000146132],"category_scores_gemma":[0.0001347363,0.000151786,0.00007599373,0.0001577558,0.000468135,0.00001400682,0.0002557795,0.00009783138,0.00002526173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002433561,"about_ca_system_score_gemma":0.0002083467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001772967,"about_ca_topic_score_gemma":0.0001104608,"domain_scores_codex":[0.9982294,0.00007264299,0.0008438712,0.0002022492,0.0002959729,0.0003558748],"domain_scores_gemma":[0.9979703,0.0000683117,0.0006523874,0.001016323,0.0001921488,0.0001004818],"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":[0.001683753,0.001803683,0.1385167,0.000807356,0.001610828,0.000003246507,0.005547805,0.008358818,0.6625514,0.003276656,0.02916936,0.1466704],"study_design_scores_gemma":[0.001962777,0.00230269,0.01798308,0.00007072362,0.0001584675,0.000006905013,0.001264765,0.03039279,0.6772775,0.00003298925,0.2678502,0.0006971153],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684794,0.0008290503,0.02774478,0.00003039529,0.0002149842,0.0004050988,0.001819393,0.000009261618,0.000467632],"genre_scores_gemma":[0.990314,0.0001991864,0.006189802,0.00002795852,0.00009657402,0.00001016535,0.003094411,0.00002429535,0.00004362117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2386809,"threshold_uncertainty_score":0.6189654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719648225129599,"score_gpt":0.2739516866853645,"score_spread":0.2567552044340685,"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."}}