{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009499087,0.003309155,0.001913881,0.004659533,0.002028484,0.004124656,0.004577726,0.001356426,0.02647264],"category_scores_gemma":[0.01559337,0.002235582,0.003152293,0.004305152,0.001124062,0.003408459,0.007637373,0.00386409,0.02653095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001605626,"about_ca_system_score_gemma":0.0048563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007152312,"about_ca_topic_score_gemma":0.006236257,"domain_scores_codex":[0.9956589,0.000638954,0.0004439676,0.001334198,0.001520849,0.0004031662],"domain_scores_gemma":[0.9939945,0.001783464,0.0004944821,0.001949086,0.001242821,0.0005355796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001813069,0.0002268114,0.009371953,0.001615785,0.0007074678,0.000783959,0.001139822,0.006209657,0.03711927,0.007309057,0.7921094,0.1415938],"study_design_scores_gemma":[0.0009423057,0.0003247239,0.02819274,0.0005843531,0.0005112884,0.001626149,0.0006140928,0.07931459,0.1166731,0.05133773,0.7189645,0.0009143326],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.00618543,0.0004534682,0.3259123,0.0009350036,0.0003389333,0.0007179766,0.1350207,0.5262404,0.004195693],"genre_scores_gemma":[0.04206255,0.0005097128,0.3904837,0.001445356,0.0001875036,0.002762354,0.4759863,0.08124099,0.005321394],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02647264,"threshold_uncertainty_score":0.08855987,"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."}}