{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007971639,0.002712688,0.00174688,0.003891638,0.001752263,0.002916848,0.003360754,0.002141261,0.004446509],"category_scores_gemma":[0.01396302,0.001578314,0.0018093,0.003275463,0.001566623,0.003039567,0.00293151,0.004001028,0.003488679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173818,"about_ca_system_score_gemma":0.00221315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002045138,"about_ca_topic_score_gemma":0.003064302,"domain_scores_codex":[0.997221,0.0007061748,0.0001643509,0.001043497,0.0007364755,0.000128533],"domain_scores_gemma":[0.9959543,0.00224114,0.0004929393,0.0007622174,0.0004201481,0.000129214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001469776,0.0004289485,0.008908194,0.001103294,0.001107363,0.0005033634,0.001010233,0.07844109,0.1105176,0.02210025,0.03236259,0.7420474],"study_design_scores_gemma":[0.0002091582,0.0002607409,0.002427717,0.0001123375,0.0001629851,0.0003729969,0.0001507377,0.8095841,0.07770547,0.07703705,0.03180164,0.0001750206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002951383,0.0001450135,0.9767681,0.00006934597,0.00004207104,0.00007780154,0.0006308313,0.0190207,0.0002947185],"genre_scores_gemma":[0.02115888,0.0001021826,0.9735761,0.0001422474,0.0000380004,0.0002436256,0.001729447,0.002455636,0.0005538533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007971639,"threshold_uncertainty_score":0.0421586,"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."}}