{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008580553,0.0001855137,0.0002535263,0.00007243245,0.0005778202,0.00004152159,0.0003159401,0.0001163204,0.00001595178],"category_scores_gemma":[0.00001077214,0.000188916,0.0001412486,0.000139072,0.00001934408,0.000004980793,0.0002220014,0.0001441618,0.000002928507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003917322,"about_ca_system_score_gemma":0.00009740242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004498941,"about_ca_topic_score_gemma":0.00002179043,"domain_scores_codex":[0.9983353,0.0001517294,0.0003127278,0.0005754339,0.0001171319,0.0005076663],"domain_scores_gemma":[0.9992529,0.000006302005,0.0001346797,0.000424394,0.00006587534,0.0001159145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009566269,0.0001687537,0.0009807983,0.0001992977,0.00004356199,8.66337e-7,0.0002334294,0.0003797254,0.9948721,0.00009350967,0.002717213,0.0002150754],"study_design_scores_gemma":[0.003323934,0.001431418,0.0003324399,0.00002245718,0.00009356532,0.0002373219,0.007549401,0.01245917,0.2357146,0.00001224024,0.7379053,0.0009181869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8342089,0.003302398,0.1560795,0.0001528055,0.0007806696,0.002853136,0.001249025,0.0001072589,0.001266271],"genre_scores_gemma":[0.9631512,0.000006840335,0.02959,0.0004042687,0.0005197152,0.000488309,0.003246882,0.00006620972,0.002526587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7591575,"threshold_uncertainty_score":0.770377,"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."}}