{"id":"W4312192991","doi":"10.1038/s41467-022-35564-z","title":"Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of General Medical Sciences; Turun yliopiston tutkijakoulu; European Regional Development Fund; National Institutes of Health; Turun Yliopisto; National Institute of Allergy and Infectious Diseases; Fundacja na rzecz Nauki Polskiej; European Commission","keywords":"Proteomics; Computer science; Benchmarking; Ranking (information retrieval); Data mining; Missing data; Protein expression; Differential (mechanical device); Expression (computer science); Artificial intelligence; Machine learning; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02168351,0.002079385,0.001656871,0.003076466,0.0006609855,0.002518686,0.002566907,0.001244149,0.001588224],"category_scores_gemma":[0.04141871,0.000640101,0.002165243,0.00255807,0.001000453,0.001813402,0.002427659,0.002430006,0.001160511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001308853,"about_ca_system_score_gemma":0.001827725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001698295,"about_ca_topic_score_gemma":0.001727252,"domain_scores_codex":[0.9889915,0.004101812,0.001031148,0.002234484,0.003231574,0.0004095633],"domain_scores_gemma":[0.9804475,0.01120506,0.002096234,0.003118464,0.002878579,0.0002541615],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009224363,0.0006701041,0.02184991,0.001367015,0.001455479,0.0003934679,0.0004300202,0.3936541,0.1380527,0.01933656,0.008242867,0.4136254],"study_design_scores_gemma":[0.0000434799,0.000282569,0.006266626,0.00006764235,0.00009706171,0.0001759929,0.00008506005,0.9231211,0.05573321,0.008107401,0.00591739,0.0001024479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01461376,0.0003385609,0.9792662,0.0001250656,0.0000377973,0.00008746971,0.000418629,0.004556958,0.0005555507],"genre_scores_gemma":[0.219916,0.0003880906,0.7737663,0.0002134492,0.00004594602,0.0006789317,0.00259097,0.001621325,0.0007789906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9783165,"threshold_uncertainty_score":0.1146747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09565939667920258,"score_gpt":0.342872405674108,"score_spread":0.2472130089949054,"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."}}