{"id":"W3039150496","doi":"10.3389/fgene.2020.00654","title":"Gene Set Analysis: Challenges, Opportunities, and Future Research","year":2020,"lang":"en","type":"review","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":224,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Computer science; Strengths and weaknesses; Class (philosophy); Data set; Data mining; Data science; Computational biology; Biology; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006899025,0.0004250255,0.001265471,0.0004367537,0.00008639823,0.00006371486,0.0005373725,0.0009233312,0.000006660237],"category_scores_gemma":[0.0000135234,0.0004012747,0.0003052503,0.000523163,0.000176355,0.000002379945,0.0004786372,0.0006262664,0.000003783056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005535756,"about_ca_system_score_gemma":0.0003824644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002750049,"about_ca_topic_score_gemma":0.00002080945,"domain_scores_codex":[0.9976213,0.000310881,0.000651159,0.0006312876,0.0002616839,0.0005236758],"domain_scores_gemma":[0.9987347,0.0000153277,0.0002022441,0.0007210564,0.00007739841,0.0002492974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008328325,0.00001320603,0.00001015862,0.001675549,0.0009793636,0.00002355189,0.0001109095,0.000005269701,4.205767e-7,0.00004976935,0.0220284,0.9750951],"study_design_scores_gemma":[0.0001481381,0.0001209077,0.000005924566,0.0001992376,0.0007393582,0.00001813865,0.0005253431,0.0002101381,0.000001967133,0.00008402472,0.9975418,0.0004049774],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000005085462,0.9970809,0.0005551291,0.0002080838,0.0005544202,0.0005006674,0.0002123458,0.000007635032,0.0008757915],"genre_scores_gemma":[0.000005616322,0.9894556,0.007075463,0.0000734202,0.001532759,0.00006275624,0.00148279,0.00006262875,0.0002489615],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9755135,"threshold_uncertainty_score":0.9998439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1091931569587053,"score_gpt":0.3370184818457165,"score_spread":0.2278253248870112,"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."}}