{"id":"W4304893270","doi":"10.3389/fimmu.2022.977470","title":"The human milk proteome and allergy of mother and child: Exploring associations with protein abundances and protein network connectivity","year":2022,"lang":"en","type":"article","venue":"Frontiers in Immunology","topic":"Infant Nutrition and Health","field":"Nursing","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Hospital for Sick Children; SickKids Foundation; University of Toronto; University of Alberta; University of Manitoba; Children's Hospital Research Institute of Manitoba","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institutes of Health Research; AllerGen; University of Alberta; University of Toronto; McMaster University","keywords":"Proteome; Allergy; Food allergy; Human proteins; Biology; Human proteome project; Immunology; Computational biology; Proteomics; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000347863,0.0002247294,0.0002720288,0.001215821,0.0003795627,0.0005116957,0.0002360354,0.0002610312,0.001095512],"category_scores_gemma":[0.001203956,0.0001085052,0.000320764,0.001565742,0.000225179,0.000278738,0.0004778019,0.0002953328,0.0001322689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006865757,"about_ca_system_score_gemma":0.000538011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02847004,"about_ca_topic_score_gemma":0.04285519,"domain_scores_codex":[0.9998355,0.00002419061,0.000007236438,0.00005666378,0.00003000859,0.00004634203],"domain_scores_gemma":[0.9993972,0.0001603365,0.0002356985,0.00002906417,0.00007842561,0.0000993454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002543631,0.00002119998,0.9875551,0.00006746412,0.0002825801,0.0001320765,0.0002005193,0.000329849,0.005178594,0.00009412241,0.0003284171,0.005555648],"study_design_scores_gemma":[0.000001429922,0.00001530115,0.9985012,0.000004075934,0.00003294117,0.00008641536,0.0001096978,0.0007885975,0.0001864645,0.00008298953,0.0001885357,0.000002354811],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976364,0.000574091,0.0002903646,0.00007079105,0.000002880131,0.000005159181,0.001047011,0.000007393585,0.0003658823],"genre_scores_gemma":[0.9980339,0.0003408986,0.0005330576,0.00001616121,0.000004997426,0.00000990122,0.0009388016,0.000003550841,0.000118692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02847004,"threshold_uncertainty_score":0.05660868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453819020608991,"score_gpt":0.2343147058547387,"score_spread":0.2197765156486488,"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."}}