{"id":"W4281909951","doi":"10.1093/bioinformatics/btac355","title":"MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Normalization (sociology); Computer science; Fold (higher-order function); Statistics; Mathematics","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":[0.01157637,0.003645661,0.002284482,0.003750334,0.001538594,0.003062448,0.003942807,0.001717503,0.01246052],"category_scores_gemma":[0.0343176,0.001643024,0.002514435,0.003212176,0.001326601,0.002573644,0.002439593,0.003041361,0.008532084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001698517,"about_ca_system_score_gemma":0.002109279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004564764,"about_ca_topic_score_gemma":0.005389729,"domain_scores_codex":[0.9944759,0.00167187,0.0004095331,0.001886651,0.001349645,0.0002063864],"domain_scores_gemma":[0.9924493,0.003888261,0.001006337,0.0012433,0.001278164,0.000134604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001820349,0.000364794,0.03733496,0.004200038,0.002358937,0.0006896658,0.001393343,0.0459027,0.1036445,0.01692248,0.1458641,0.6395041],"study_design_scores_gemma":[0.0003491754,0.0007153272,0.05562524,0.0006975771,0.0006460788,0.001657518,0.0002761534,0.4805678,0.1943088,0.03198205,0.2320858,0.001088449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01249366,0.001395235,0.9132091,0.0003183533,0.0003042715,0.0004703391,0.007765878,0.06158493,0.00245823],"genre_scores_gemma":[0.05568761,0.0005984493,0.9090949,0.000265461,0.0001006039,0.002427133,0.01329353,0.01572886,0.002803355],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01246052,"threshold_uncertainty_score":0.06122243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02548739959916625,"score_gpt":0.246409569521064,"score_spread":0.2209221699218978,"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."}}