{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005467778,0.001907287,0.001787288,0.003856913,0.001158615,0.001958212,0.002754674,0.001278238,0.03130593],"category_scores_gemma":[0.01435029,0.001541961,0.001592918,0.003524179,0.0006604191,0.002113807,0.004260744,0.002310439,0.01338049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000887066,"about_ca_system_score_gemma":0.002306539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003743181,"about_ca_topic_score_gemma":0.008370153,"domain_scores_codex":[0.9978605,0.0004700946,0.0001621981,0.0007196732,0.00059296,0.0001945236],"domain_scores_gemma":[0.9952793,0.002342891,0.0003781443,0.00118015,0.0005467578,0.000272799],"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":[0.002516764,0.0002032447,0.02702086,0.00332869,0.002062639,0.0008806922,0.0008274545,0.007215879,0.05881913,0.01117292,0.7568558,0.1290959],"study_design_scores_gemma":[0.001296668,0.0003181632,0.05127047,0.0003953368,0.0006951382,0.001632178,0.0003675261,0.1148177,0.1714134,0.03432281,0.6228043,0.0006663873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.01715452,0.0008933354,0.3205574,0.0005712867,0.0002978414,0.0004213991,0.2250112,0.4288727,0.006220296],"genre_scores_gemma":[0.07540864,0.0005114238,0.5015629,0.001010508,0.0001549653,0.002859361,0.3576832,0.05552648,0.005282429],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03130593,"threshold_uncertainty_score":0.1047288,"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."}}