{"id":"W2122217863","doi":"10.15252/msb.20145760","title":"Efficient sample processing for proteomics applications—Are we there yet?","year":2014,"lang":"en","type":"letter","venue":"Molecular Systems Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"","keywords":"Proteomics; Biology; Computational biology; Sample (material); Quantitative proteomics; Throughput; Scalability; Computer science; Chromatography; Genetics; Telecommunications; Database","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":[0.0106334,0.0008205093,0.001069201,0.0005077878,0.001869883,0.003478436,0.001713614,0.01205664,0.005560617],"category_scores_gemma":[0.02009634,0.0006071418,0.0006187453,0.000498895,0.005294145,0.004714374,0.001822225,0.01858487,0.009527645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003248462,"about_ca_system_score_gemma":0.001832843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008023753,"about_ca_topic_score_gemma":0.001453959,"domain_scores_codex":[0.993101,0.001854319,0.0005308097,0.0006368681,0.003409506,0.0004673957],"domain_scores_gemma":[0.9870998,0.006471968,0.00095737,0.001134699,0.003204127,0.0011321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002352559,0.00008923678,0.0007527587,0.0005212798,0.00003886161,0.001933501,0.0001285943,0.0001735502,0.007726915,0.01869563,0.8074315,0.1622729],"study_design_scores_gemma":[0.00008573817,0.0001448315,0.0006910157,0.000251574,0.00001872511,0.003401128,0.0001832316,0.0006319844,0.003674848,0.03311528,0.9577437,0.000057992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0008088679,0.02182317,0.005978623,0.9401786,0.02746759,0.00005519181,0.00007980119,0.0002355512,0.003372585],"genre_scores_gemma":[0.02091656,0.05026587,0.02117984,0.8240774,0.06902685,0.0003245003,0.0002211535,0.0001767592,0.01381119],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01205664,"threshold_uncertainty_score":0.05623543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01989444454813701,"score_gpt":0.2872399825376494,"score_spread":0.2673455379895124,"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."}}