{"id":"W4323035871","doi":"10.1101/2023.02.28.530481","title":"MEDIPIPE: an automated and comprehensive pipeline for cfMeDIP-seq data quality control and analysis","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Cancer Research Institute","keywords":"MIT License; Computer science; Pipeline (software); Profiling (computer programming); Data mining; Software; Data quality; Programming language; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008594993,0.003383492,0.002810526,0.003926921,0.002429137,0.004131572,0.004109416,0.001761328,0.04864233],"category_scores_gemma":[0.01185499,0.003153802,0.003149521,0.002330919,0.001366464,0.002602629,0.004590803,0.006069997,0.03333979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001555814,"about_ca_system_score_gemma":0.004647222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003625318,"about_ca_topic_score_gemma":0.005883364,"domain_scores_codex":[0.995533,0.0006625594,0.0004975343,0.001600513,0.001298352,0.0004081003],"domain_scores_gemma":[0.9955155,0.001991705,0.000407488,0.0008485962,0.0008552054,0.0003814239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002231954,0.0001507777,0.004544897,0.004212655,0.0009196629,0.0006039784,0.0008980741,0.007521441,0.1243493,0.007216129,0.6920033,0.1553478],"study_design_scores_gemma":[0.001376235,0.0003218081,0.01604491,0.0008155426,0.000516231,0.001125776,0.0003083074,0.1218981,0.2303788,0.03862861,0.5874904,0.001095382],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.006513169,0.001245598,0.4563971,0.001057691,0.0006017738,0.0009138923,0.1232993,0.4063886,0.003582965],"genre_scores_gemma":[0.02740119,0.0008191561,0.7034323,0.001553804,0.0003176017,0.00431612,0.1823942,0.07422604,0.005539649],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04864233,"threshold_uncertainty_score":0.1627249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05069475395502206,"score_gpt":0.3300662198933673,"score_spread":0.2793714659383452,"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."}}