{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008339934,0.003233191,0.002224024,0.003429376,0.002313079,0.003741586,0.004097681,0.001783184,0.04008136],"category_scores_gemma":[0.01294807,0.002869316,0.002779415,0.002273109,0.001234428,0.002698949,0.004537411,0.005507341,0.0293801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515622,"about_ca_system_score_gemma":0.004934463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004347923,"about_ca_topic_score_gemma":0.007460725,"domain_scores_codex":[0.996436,0.0005306236,0.0003907795,0.001266255,0.001032613,0.0003437456],"domain_scores_gemma":[0.9952413,0.002015431,0.0004433372,0.0008718026,0.0009882738,0.000439857],"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":[0.002960702,0.00022862,0.007218957,0.003727819,0.001213658,0.0007238425,0.001398039,0.006819497,0.1280985,0.006930158,0.6617325,0.1789476],"study_design_scores_gemma":[0.001280425,0.0004127332,0.01951536,0.0006332592,0.000544426,0.001233822,0.0003995974,0.1539299,0.2347171,0.03110093,0.5550287,0.001203756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.005772624,0.0007531327,0.4016473,0.0007764442,0.0003809276,0.0008350479,0.0682809,0.5184281,0.003125451],"genre_scores_gemma":[0.03041006,0.0006745561,0.7163883,0.001596164,0.0002081734,0.003830844,0.1521977,0.08861941,0.006074793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04008136,"threshold_uncertainty_score":0.1340855,"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."}}