{"id":"W2998913628","doi":"10.1038/s41467-019-13973-x","title":"Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":362,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"Canadian Institutes of Health Research; Carl Friedrich von Siemens Stiftung; Bundesministerium für Bildung und Forschung; Government of Ontario; National Natural Science Foundation of China; Government of Canada; Deutsche Forschungsgemeinschaft; Ontario Genomics Institute; China Scholarship Council; Ontario Genomics; Genome Canada; Alexander von Humboldt-Stiftung","keywords":"Proteome; Chromatography; Tandem mass spectrometry; Chemistry; Mass spectrometry; Reproducibility; Coefficient of variation; Proteomics; Quantitative proteomics; Tandem; Analytical Chemistry (journal); Materials science; Biochemistry","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":[0.0001364384,0.0001199298,0.0003267046,0.00007542237,0.0001230038,0.00001083708,0.0007165267,0.0001951513,0.00008423359],"category_scores_gemma":[0.0002364333,0.0001183324,0.0001057976,0.0008817904,0.0002326843,0.00006631092,0.0003036864,0.0005497218,8.192514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001653941,"about_ca_system_score_gemma":0.00003005393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003384319,"about_ca_topic_score_gemma":0.0000213917,"domain_scores_codex":[0.9990982,0.00003087012,0.0003543424,0.0003070613,0.0001078351,0.0001017345],"domain_scores_gemma":[0.9974474,0.0001827508,0.0003380821,0.001745616,0.0002291314,0.00005696851],"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":[0.00003358035,0.0002233932,0.007448688,0.0001171797,0.0005763833,1.191486e-7,0.0009724119,0.0005113625,0.9727034,0.01270822,0.003799165,0.0009061028],"study_design_scores_gemma":[0.0003258995,0.00005960672,0.0007162074,0.00006135584,0.0007275451,0.000001088702,0.0003555103,0.02047471,0.9375101,0.001251928,0.03821369,0.0003023788],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6477559,0.08743404,0.1644316,0.04891197,0.00003119073,0.002917043,0.007261079,0.0008997651,0.04035744],"genre_scores_gemma":[0.6229045,0.001631169,0.3748714,0.00008187873,0.000007579082,0.0001181939,0.0002963631,0.00001439803,0.00007447209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2104398,"threshold_uncertainty_score":0.4825454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03653844875497578,"score_gpt":0.3262155515964422,"score_spread":0.2896771028414664,"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."}}