{"id":"W2120604708","doi":"10.1093/nar/gkl784","title":"MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes","year":2006,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Proteome; Biology; Proteomics; Biomarker discovery; Sequence database; Computational biology; Mascot; Bioinformatics; Genetics; Gene","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":[0.001875805,0.005269888,0.005311961,0.01307421,0.002338373,0.005467073,0.005055763,0.002715278,0.03351524],"category_scores_gemma":[0.00455189,0.001979572,0.002096982,0.01992504,0.000562847,0.005812491,0.005472632,0.002109415,0.03876872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009805551,"about_ca_system_score_gemma":0.003453935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00254626,"about_ca_topic_score_gemma":0.001920812,"domain_scores_codex":[0.9985606,0.0002359495,0.0002522634,0.000354989,0.0003584211,0.0002378542],"domain_scores_gemma":[0.9983347,0.0002258087,0.0004109704,0.0004027803,0.0002429573,0.0003828999],"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":[0.003366415,0.0002033306,0.004185773,0.01610351,0.001137571,0.001833558,0.0008700821,0.002781174,0.04874244,0.01041392,0.8231236,0.08723862],"study_design_scores_gemma":[0.0002433969,0.0001237334,0.01432694,0.0005839491,0.0003743349,0.0009980993,0.0001603905,0.00266026,0.01023125,0.007734824,0.9623629,0.0001999251],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.00599948,0.007741228,0.01550237,0.0003464787,0.000224872,0.0002198776,0.9421794,0.02279459,0.004991734],"genre_scores_gemma":[0.005811519,0.002686253,0.01586442,0.0001323339,0.00005841226,0.0005301248,0.9724731,0.001336224,0.001107583],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03351524,"threshold_uncertainty_score":0.1121197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02712581743055077,"score_gpt":0.3254999163974305,"score_spread":0.2983740989668797,"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."}}