{"id":"W4388556708","doi":"10.1093/bioinformatics/btad673","title":"hipFG: high-throughput harmonization and integration pipeline for functional genomics data","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Weston Brain Institute; National Institute on Aging; Alzheimer’s Research UK; Alzheimer's Association","keywords":"Pipeline (software); Harmonization; Throughput; Computer science; Genomics; Functional genomics; Data integration; Computational biology; Data mining; Biology; Programming language; Genome; Operating system; Gene; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004134685,0.00009185017,0.0001027693,0.00004490047,0.0001193107,0.00002481949,0.0001202996,0.0001092532,0.000004430093],"category_scores_gemma":[0.0004358362,0.00008543156,0.00001996946,0.00009311949,0.00003481197,0.00001203061,0.0001707137,0.00003596316,0.00002199053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001436633,"about_ca_system_score_gemma":0.00005950199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008745335,"about_ca_topic_score_gemma":0.00002566983,"domain_scores_codex":[0.999316,0.00001739532,0.0002880204,0.0001604094,0.00005871761,0.0001594908],"domain_scores_gemma":[0.9993898,0.00004114096,0.000123623,0.0003038227,0.000103254,0.00003831567],"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":[0.0001859295,0.00009311296,0.008482717,0.0001651955,0.0001880274,3.757478e-7,0.0004192757,0.004809456,0.04245653,0.003183853,0.8014701,0.1385454],"study_design_scores_gemma":[0.001172859,0.0002591958,0.02610634,0.000008771379,0.00005758028,0.000008690981,0.0005078407,0.8520218,0.00264395,0.00124392,0.1156799,0.0002891792],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1589884,0.00009875466,0.8387131,0.0009373823,0.000359502,0.0002973309,0.0004274919,0.00003521853,0.000142852],"genre_scores_gemma":[0.7989309,0.001507865,0.1481104,0.00112522,0.0008270546,0.00006192101,0.04772053,0.00003549778,0.001680614],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8472124,"threshold_uncertainty_score":0.3483798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05108167327523701,"score_gpt":0.2834603539316874,"score_spread":0.2323786806564505,"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."}}