{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003251202,0.001167928,0.0009570892,0.0006444787,0.0005150494,0.001149692,0.001705064,0.0005470411,0.002408173],"category_scores_gemma":[0.002715781,0.0008124681,0.0006991293,0.0006560958,0.0007679844,0.0005907884,0.001068837,0.001045397,0.001265844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115712,"about_ca_system_score_gemma":0.002470921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001018627,"about_ca_topic_score_gemma":0.001092903,"domain_scores_codex":[0.9982595,0.0003129622,0.0001174625,0.0005368626,0.0005707408,0.0002025084],"domain_scores_gemma":[0.9991077,0.0003099632,0.0001000652,0.0001219895,0.0002661792,0.00009413139],"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":[0.001712,0.0008408659,0.006175787,0.0007765585,0.0001392921,0.000363307,0.0001455987,0.1573652,0.6828663,0.01762514,0.002230292,0.1297597],"study_design_scores_gemma":[0.0002429998,0.00109299,0.002898849,0.00001829012,0.00006532134,0.00008770203,0.00003601578,0.7927226,0.1868842,0.008339106,0.007550618,0.00006141296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02723675,0.00007087397,0.9670857,0.0000702448,0.00002398686,0.001512954,0.0006669346,0.002459951,0.0008725795],"genre_scores_gemma":[0.1062644,0.0001242166,0.8861123,0.0001013251,0.0000138786,0.005176685,0.001219784,0.0002238444,0.0007635286],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003251202,"threshold_uncertainty_score":0.01719427,"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."}}