{"id":"W4290805633","doi":"10.1128/msystems.00381-22","title":"MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening","year":2022,"lang":"en","type":"article","venue":"mSystems","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Ontario Genomics; Ontario Ministry of Economic Development and Innovation; Genome Canada","keywords":"Microbiome; Metaproteomics; Proteome; Computational biology; Inflammatory bowel disease; Proteomics; Metagenomics; Disease; Biology; Bioinformatics; Medicine; Pathology; Genetics","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":[0.002251826,0.001987419,0.001758855,0.001900262,0.0009128446,0.001811836,0.001858859,0.001200892,0.00420815],"category_scores_gemma":[0.003481768,0.0008845048,0.002567096,0.001217943,0.0005634707,0.001414484,0.002133812,0.001602155,0.001190167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006960419,"about_ca_system_score_gemma":0.001875141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00226546,"about_ca_topic_score_gemma":0.005043631,"domain_scores_codex":[0.9994175,0.0001624946,0.0000312839,0.0001665566,0.0001667193,0.00005537855],"domain_scores_gemma":[0.9989662,0.0004954429,0.0001614192,0.0001567719,0.0001191882,0.0001010599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004032697,0.00133876,0.03525672,0.002245071,0.003317594,0.001243497,0.0005949536,0.4787098,0.2125952,0.01946185,0.02441967,0.2167843],"study_design_scores_gemma":[0.0000943661,0.0002135829,0.001711465,0.0000194195,0.0001076684,0.0001546792,0.00005417253,0.9694968,0.01460034,0.008476996,0.005002344,0.00006818255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08153596,0.0006991724,0.8712727,0.0004777459,0.0001694674,0.0004754975,0.006865034,0.03614708,0.002357353],"genre_scores_gemma":[0.1721852,0.0003931915,0.8173197,0.0003278345,0.00005976958,0.0006534561,0.006018387,0.002042833,0.0009995336],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00420815,"threshold_uncertainty_score":0.01407766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02168740183764356,"score_gpt":0.2739446944894495,"score_spread":0.2522572926518059,"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."}}