{"id":"W4392860529","doi":"10.1101/2024.03.13.584844","title":"Assessing fecal metaproteomics workflow and small protein recovery using DDA and DIA PASEF mass spectrometry","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Health Canada","funders":"Health Canada; Government of Canada","keywords":"Metaproteomics; Workflow; Feces; Differential centrifugation; Computational biology; Biology; Computer science; Bioinformatics; Metagenomics; Microbiology; Biochemistry; Database","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":[0.003834273,0.001503695,0.0009204311,0.001823199,0.0007899939,0.001741707,0.0008278704,0.001016666,0.00177412],"category_scores_gemma":[0.003660201,0.0005363408,0.001094495,0.001216542,0.0006613614,0.001224726,0.001332285,0.001383275,0.001069946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005301499,"about_ca_system_score_gemma":0.0009635873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008172277,"about_ca_topic_score_gemma":0.001486424,"domain_scores_codex":[0.9972076,0.0004317414,0.000260331,0.0006887568,0.00118516,0.0002264007],"domain_scores_gemma":[0.9982339,0.0004002751,0.0003475553,0.0002649124,0.0006316514,0.0001215816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007967211,0.0002525573,0.01667095,0.0008530924,0.0002798731,0.0001714435,0.0001922208,0.001046441,0.9527138,0.0003357254,0.0005792266,0.02610796],"study_design_scores_gemma":[0.00003787928,0.0008896532,0.04034474,0.000153744,0.0002715103,0.0007815534,0.0002980704,0.01274431,0.9357082,0.0007288862,0.007922725,0.0001186753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7939292,0.006028879,0.1850609,0.0007151241,0.0002872158,0.00065734,0.007111873,0.002479568,0.003729777],"genre_scores_gemma":[0.7041711,0.004609418,0.2757257,0.001108587,0.0001292142,0.001135318,0.009192063,0.001082432,0.002846172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003834273,"threshold_uncertainty_score":0.02027786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832322978142137,"score_gpt":0.2429489565184088,"score_spread":0.2246257267369875,"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."}}