{"id":"W4403362860","doi":"10.1021/acs.jproteome.3c00845","title":"GraphPI: Efficient Protein Inference with Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Graph; Artificial neural network; Artificial intelligence; Computational biology; Machine learning; Theoretical computer science; Biology","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":[0.0009519573,0.001279939,0.0008733552,0.001240255,0.0006411579,0.001160716,0.002819953,0.001403974,0.003193826],"category_scores_gemma":[0.003681962,0.0007204243,0.0008713814,0.001280431,0.0006901724,0.002354476,0.001575676,0.00233793,0.00135781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001447368,"about_ca_system_score_gemma":0.002136979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01398782,"about_ca_topic_score_gemma":0.02422223,"domain_scores_codex":[0.9996613,0.00007474752,0.00001205568,0.0001184786,0.00009806691,0.000035306],"domain_scores_gemma":[0.9991948,0.0003904845,0.00007395948,0.0001736937,0.0001175608,0.00004950487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001874924,0.0001670313,0.001856905,0.0002699244,0.0001431719,0.0001387626,0.00008146515,0.6912184,0.005774519,0.02581564,0.01444974,0.2598969],"study_design_scores_gemma":[0.000006007386,0.000007624322,0.00005937395,0.000003473766,0.000003434003,0.000008010111,0.000003386297,0.990047,0.0007139025,0.008543874,0.0006010088,0.000002947399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01182487,0.0003791739,0.9737685,0.0003476699,0.0000672512,0.00009278913,0.0006604905,0.01110141,0.001757756],"genre_scores_gemma":[0.2242221,0.0005577703,0.7636999,0.0005519133,0.00009769954,0.0003133947,0.004654781,0.00109961,0.004802833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01398782,"threshold_uncertainty_score":0.02781278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133530425597834,"score_gpt":0.3802096852486129,"score_spread":0.3388743809926346,"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."}}