{"id":"W4406713780","doi":"10.1093/bioadv/vbae209","title":"<u>Imp</u>utation for <u>Li</u>pidomics and <u>Met</u>abolomics (ImpLiMet): a web-based application for optimization and method selection for missing data imputation","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational Cancer Research Centre; University of Toronto; McGill Genome Centre; National Research Council Canada; McGill University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Computer science; Mathematics; Statistics","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":[0.008406925,0.003169103,0.00304155,0.003212138,0.001992697,0.004705795,0.004611419,0.002876922,0.2249493],"category_scores_gemma":[0.03130928,0.002253625,0.003563582,0.0043432,0.00128758,0.00356732,0.006304353,0.003836476,0.1320377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478085,"about_ca_system_score_gemma":0.003403348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003116429,"about_ca_topic_score_gemma":0.005046152,"domain_scores_codex":[0.99633,0.0008512606,0.0002916353,0.0009123504,0.001283407,0.0003314442],"domain_scores_gemma":[0.9897612,0.005305578,0.0009947664,0.002149121,0.001240365,0.0005489194],"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":[0.00091093,0.0001191835,0.003911252,0.002016441,0.0003942697,0.0006342753,0.0002928351,0.003547953,0.004601014,0.008136324,0.8979949,0.07744048],"study_design_scores_gemma":[0.0009244058,0.0002350481,0.006169768,0.0009632919,0.0002530745,0.001296162,0.0001660635,0.06593627,0.03854505,0.06098047,0.824046,0.0004843514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.004179537,0.001112062,0.2159374,0.002175099,0.001523721,0.0003947366,0.2039749,0.5549043,0.01579824],"genre_scores_gemma":[0.04078936,0.001669485,0.3526665,0.004087312,0.0007821752,0.002688708,0.3154034,0.2603877,0.02152525],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.2249493,"threshold_uncertainty_score":0.7525304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05619849722513385,"score_gpt":0.4230035011006127,"score_spread":0.3668050038754789,"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."}}