{"id":"W2120649366","doi":"10.1142/s0219720008003321","title":"DESIGN AND ANALYSIS OF QUANTITATIVE DIFFERENTIAL PROTEOMICS INVESTIGATIONS USING LC-MS TECHNOLOGY","year":2008,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Pipeline (software); Pooling; Computer science; Biomarker discovery; Proteomics; Quantitative proteomics; Data mining; Computational biology; Identification (biology); Deconvolution; Artificial intelligence; Biology; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0000897323,0.00008549756,0.0002907615,0.0003749553,0.0001152033,0.000007433179,0.00008520013,0.0001025441,0.000009972848],"category_scores_gemma":[0.00004532759,0.00007188873,0.0000526589,0.0003136626,0.0003633522,0.00008420611,0.00004612466,0.0001328471,1.238621e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001756068,"about_ca_system_score_gemma":0.00008107179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002864501,"about_ca_topic_score_gemma":2.348182e-7,"domain_scores_codex":[0.9992076,0.00001184608,0.0005654378,0.00006389667,0.0000696311,0.0000816239],"domain_scores_gemma":[0.9987873,0.0001485458,0.0007063362,0.00006181352,0.0002506723,0.00004533797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002873201,0.0003588457,0.04945229,0.0002698459,0.003507833,0.000007192256,0.003381671,0.2469718,0.4235281,0.2641622,0.00005118007,0.00802167],"study_design_scores_gemma":[0.0003744816,0.0001615228,0.0007651672,0.00002807754,0.0002467413,0.0001763621,0.0001605947,0.9187155,0.01605158,0.06316711,0.00003568597,0.000117198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4726763,0.00005499226,0.5271153,0.00006610152,0.000004503461,0.00004291565,0.00001994036,0.000004755756,0.00001523055],"genre_scores_gemma":[0.4189281,0.0001039535,0.5809249,0.0000136631,0.00000594492,0.00000292748,0.00001650475,0.000002543096,0.000001450934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6717436,"threshold_uncertainty_score":0.2931538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03741714244687239,"score_gpt":0.3012285470245092,"score_spread":0.2638114045776369,"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."}}