{"id":"W1909028472","doi":"10.1186/s13015-015-0055-3","title":"Erratum to: Inferring interaction type in gene regulatory networks using co-expression data","year":2015,"lang":"en","type":"erratum","venue":"Algorithms for Molecular Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Computer science; Expression (computer science); Type (biology); Data type; Computational biology; Data mining; Gene regulatory network; Data science; Gene expression; Gene; Biology; Genetics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009035511,0.0006998125,0.0008775831,0.0004386485,0.0001185099,0.00005322598,0.001266748,0.001982932,0.00001043269],"category_scores_gemma":[0.0002858274,0.0007489143,0.0002473837,0.0004738786,0.0001212117,0.00001417951,0.001232987,0.0006814605,0.000008447663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001940371,"about_ca_system_score_gemma":0.000559842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008157782,"about_ca_topic_score_gemma":0.0001138965,"domain_scores_codex":[0.9958953,0.000350412,0.0007864344,0.001840974,0.0002415661,0.0008853647],"domain_scores_gemma":[0.9963458,0.0000218189,0.0004513653,0.002482699,0.000400374,0.0002978973],"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":[0.0002554507,0.00009333362,0.0001943926,0.0000450673,0.0004652923,0.00002791713,0.00002143358,0.02738879,0.4806405,0.000008978358,0.4861784,0.00468044],"study_design_scores_gemma":[0.001597412,0.001124469,0.0001462808,0.0003792811,0.000528989,0.0001163069,0.00007269802,0.1747432,0.09553177,0.0003438461,0.7231777,0.002238029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1240641,0.05552137,0.745464,0.0002951068,0.06488354,0.00493339,0.001377501,0.0002067247,0.003254351],"genre_scores_gemma":[0.4120759,0.003976649,0.1609794,0.003592955,0.03395932,0.0006826756,0.354405,0.002092943,0.02823512],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5844846,"threshold_uncertainty_score":0.9994962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04794319886971091,"score_gpt":0.3569521048979705,"score_spread":0.3090089060282596,"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."}}