{"id":"W2405189771","doi":"10.1186/s12864-016-2642-1","title":"LOGIQA: a database dedicated to long-range genome interactions quality assessment","year":2016,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut National Du Cancer; Centre National de la Recherche Scientifique; Ligue Contre le Cancer; Université de Strasbourg; Institute of Genetics; Fondation pour la Recherche Médicale; Institut National de la Santé et de la Recherche Médicale; Alliance Nationale pour les Sciences de la Vie et de la Santé","keywords":"Genome; Quality (philosophy); Range (aeronautics); Computer science; Chromatin; Database; Data quality; Computational biology; Biology; Data science; Genetics; Gene; Metric (unit); Engineering","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":[0.006203779,0.00384112,0.00356159,0.01170136,0.001548476,0.005758316,0.005937815,0.002649767,0.02696452],"category_scores_gemma":[0.01786446,0.001766767,0.00259583,0.009971027,0.0007402929,0.004239008,0.005908937,0.003027305,0.02325604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155488,"about_ca_system_score_gemma":0.004060965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003381105,"about_ca_topic_score_gemma":0.004082093,"domain_scores_codex":[0.9955816,0.0006875636,0.0009307425,0.001152072,0.001341998,0.0003061116],"domain_scores_gemma":[0.9871113,0.004062309,0.002509925,0.003382194,0.001932046,0.001002167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005245265,0.0006459389,0.02856771,0.0152841,0.002035101,0.001287057,0.001156795,0.008374717,0.07087184,0.01150957,0.6999615,0.1550604],"study_design_scores_gemma":[0.001369152,0.0005056287,0.04306619,0.001267796,0.001004288,0.001788312,0.000434522,0.02837699,0.05718675,0.02347555,0.8407792,0.0007457145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008489913,0.001578067,0.05887257,0.0003304675,0.0001506307,0.0003706827,0.7730516,0.1528405,0.004315627],"genre_scores_gemma":[0.01266703,0.0005729546,0.04889917,0.0002250215,0.00003269256,0.0009094306,0.9262421,0.009306798,0.001144856],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02696452,"threshold_uncertainty_score":0.09020531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03573785976606091,"score_gpt":0.3192003173472143,"score_spread":0.2834624575811533,"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."}}