{"id":"W4399815103","doi":"10.1101/2024.06.17.599353","title":"Imputation for Lipidomics and Metabolomics (ImpLiMet): Online application for optimization and method selection for missing data imputation","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Ottawa; National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Missing data; Computer science; Lipidomics; Metabolomics; Genomic selection; Data mining; Data science; Bioinformatics; Machine learning; 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":[0.01067531,0.002960935,0.002135569,0.001834697,0.001027037,0.00273095,0.004701722,0.002183935,0.0727855],"category_scores_gemma":[0.03483664,0.002156233,0.004354796,0.002330354,0.0007408752,0.002286131,0.005135661,0.004247863,0.03482025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009712943,"about_ca_system_score_gemma":0.002638789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00275562,"about_ca_topic_score_gemma":0.004595811,"domain_scores_codex":[0.9961318,0.001482381,0.0004166185,0.0006736742,0.0009915013,0.0003040674],"domain_scores_gemma":[0.9886466,0.007937984,0.0005730371,0.001399868,0.001150887,0.0002916688],"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":[0.002359417,0.0003699842,0.007633702,0.003635528,0.001490337,0.0009482948,0.0007022634,0.04455857,0.007298419,0.02060045,0.5710247,0.3393783],"study_design_scores_gemma":[0.001469782,0.0002818002,0.006218613,0.0009330024,0.0003451749,0.0009363249,0.0001733441,0.6429076,0.02938109,0.06796817,0.2489122,0.0004728769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003188632,0.0005788145,0.6418363,0.0005647572,0.0003589793,0.0003065378,0.02224477,0.3284404,0.002480755],"genre_scores_gemma":[0.03348989,0.000574014,0.8344042,0.0009159762,0.0001901896,0.002362069,0.04959593,0.07265558,0.005812155],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0727855,"threshold_uncertainty_score":0.2434918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02011716711891449,"score_gpt":0.2955854359982373,"score_spread":0.2754682688793228,"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."}}