{"id":"W2886540233","doi":"10.1074/mcp.tir118.000850","title":"gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data","year":2018,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"National Cancer Institute; Alkek Center for Molecular Discovery, Baylor College of Medicine; Cancer Prevention and Research Institute of Texas; Diana Helis Henry Medical Research Foundation; Baylor College of Medicine; Robert and Janice McNair Foundation","keywords":"Proteomics; Inference; Computational biology; Computer science; Algorithm; Gene; Biology; Genetics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000382543,0.0003138426,0.0003552749,0.0001049554,0.000231391,0.00007859793,0.0007343946,0.0002560054,0.0000153579],"category_scores_gemma":[0.000189466,0.0003536727,0.00009201094,0.0002535496,0.000254792,0.0002402967,0.0004734009,0.0002773206,0.000004752389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000076176,"about_ca_system_score_gemma":0.0001268986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005933748,"about_ca_topic_score_gemma":0.000004889156,"domain_scores_codex":[0.9979323,0.00002855499,0.0005889352,0.0007962151,0.000236222,0.0004177325],"domain_scores_gemma":[0.9977868,0.00006143118,0.0004237705,0.001283318,0.0003229578,0.0001217885],"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":[0.00003786173,0.00007341529,0.0001107641,0.0001868664,0.00004234612,0.000002844759,0.0001272031,0.0000134561,0.9892742,0.003418302,0.00002537493,0.006687353],"study_design_scores_gemma":[0.0006839753,0.00009613822,0.000005445238,0.0000784026,0.00006839307,0.00001091203,0.00006217001,0.09985346,0.8911599,0.006937514,0.0006940135,0.0003496936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4026509,0.0001960799,0.5954697,0.00004691273,0.00002654956,0.001233475,0.0002239369,0.00007717518,0.00007534167],"genre_scores_gemma":[0.2537838,0.0001228396,0.7447848,0.00004747251,0.0001182214,0.0004430867,0.0005512872,0.00006832348,0.00008017894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1493151,"threshold_uncertainty_score":0.9998915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02794804127018381,"score_gpt":0.300020586429567,"score_spread":0.2720725451593832,"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."}}