{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002667472,0.0009930108,0.0007854496,0.001069092,0.0006211453,0.001253879,0.0009174079,0.0007279127,0.001288789],"category_scores_gemma":[0.001674895,0.0004536445,0.0005076484,0.0006070377,0.0007331811,0.001207719,0.0007978391,0.00108947,0.001434308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005949107,"about_ca_system_score_gemma":0.0009442173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005364075,"about_ca_topic_score_gemma":0.001092566,"domain_scores_codex":[0.998394,0.000186845,0.0001152421,0.0004789035,0.0007177153,0.0001074271],"domain_scores_gemma":[0.9991773,0.0002663441,0.0001224926,0.0001474857,0.0002274564,0.000058882],"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":[0.00005978605,0.00004294511,0.0003541803,0.0001010028,0.0000204205,0.00003324154,0.0000150451,0.0002013617,0.9879445,0.0002294062,0.0005695967,0.01042844],"study_design_scores_gemma":[0.00002402949,0.0001386503,0.003495164,0.00002344222,0.00002912569,0.0002784912,0.00002057622,0.006116584,0.982105,0.0006596449,0.007073849,0.00003530217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2714551,0.00735491,0.6918188,0.001564147,0.00064779,0.001189208,0.009889923,0.01074009,0.005340083],"genre_scores_gemma":[0.3046311,0.004747129,0.6757321,0.001074671,0.0002680375,0.001920124,0.006189864,0.0006327254,0.004804243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002667472,"threshold_uncertainty_score":0.01410711,"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."}}