{"id":"W4383186166","doi":"10.1093/bioinformatics/btad423","title":"MEDIPIPE: an automated and comprehensive pipeline for cfMeDIP-seq data quality control and analysis","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Ontario Institute for Cancer Research; Cancer Research Institute","keywords":"Computer science; MIT License; Pipeline (software); Software; Bioconductor; Data mining; Source code; Operating system; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005777394,0.0001308142,0.0002341502,0.0001048155,0.0001014655,0.0000537049,0.0001610941,0.0001330825,0.00000171499],"category_scores_gemma":[0.0002691646,0.0001169025,0.00003776131,0.0002232732,0.00008210168,0.00001632844,0.0001613066,0.00003746405,0.000002316259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003872778,"about_ca_system_score_gemma":0.00003275282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001754826,"about_ca_topic_score_gemma":0.00005787636,"domain_scores_codex":[0.9990258,0.00004736407,0.0003858107,0.0002226641,0.0001294587,0.0001889357],"domain_scores_gemma":[0.9989488,0.00009619339,0.0001499584,0.0005322926,0.0001487737,0.0001240331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001092204,0.0003588661,0.08775164,0.002174204,0.004802233,0.000006636135,0.003554229,0.004225884,0.5614274,0.0008676334,0.03340754,0.3003315],"study_design_scores_gemma":[0.001211176,0.0001392179,0.03559042,0.000004704302,0.0002714887,9.382288e-7,0.0003409102,0.9452108,0.002863051,0.00008573913,0.01407984,0.0002017518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8966626,0.001096463,0.09936129,0.0003085872,0.0001310584,0.0004908448,0.001737357,0.0001323151,0.0000794865],"genre_scores_gemma":[0.9683037,0.001333944,0.01596288,0.0003196044,0.0001599821,0.00001932781,0.01378614,0.00001893068,0.00009543791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9409848,"threshold_uncertainty_score":0.4767144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05855120257903918,"score_gpt":0.374714899196294,"score_spread":0.3161636966172548,"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."}}