{"id":"W2092415108","doi":"10.1021/pr0707509","title":"Open Access to Proteomics Data: A Valuable Resource for Biology and Medicine","year":2007,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of California, Los Angeles; School of Medicine, Vanderbilt University; Rijksuniversiteit Groningen; Imperial College London; McGill University; University of Ottawa; National Cancer Institute; Carnegie Mellon University; Université de Genève; Yale University; Vanderbilt University; Purdue University; North Carolina State University; Bristol-Myers Squibb; AstraZeneca; Lunds Universitet; Johns Hopkins University; Pfizer","keywords":"Proteomics; Resource (disambiguation); Computational biology; Data science; Computer science; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02148749,0.000135148,0.0003289573,0.0004778661,0.0002238897,0.0002694113,0.003746555,0.0002152859,0.00002438102],"category_scores_gemma":[0.007376852,0.00009456559,0.00004095078,0.0004044195,0.0004829095,0.00004517801,0.004965009,0.000431067,0.000004538029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004610639,"about_ca_system_score_gemma":0.0005766574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006672482,"about_ca_topic_score_gemma":0.00006169965,"domain_scores_codex":[0.9973185,0.0001905331,0.0007033628,0.000356645,0.0006993199,0.0007316287],"domain_scores_gemma":[0.9970936,0.0002334676,0.0001953606,0.0006433435,0.00122918,0.0006050484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002471457,0.0001316427,0.001094345,0.0002582153,0.0001193771,0.00001088453,0.0001782347,0.000003998202,0.8501056,0.0001599031,0.1055254,0.03994094],"study_design_scores_gemma":[0.003022791,0.00973092,0.001603854,0.0001991601,0.00001875868,0.00008836116,0.000662444,0.0002196944,0.1551639,0.003533259,0.8255224,0.0002343699],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.665875,0.003489182,0.2589487,0.04994448,0.000550071,0.0136094,0.0002446428,0.00001191225,0.007326587],"genre_scores_gemma":[0.6455221,0.004438303,0.3264942,0.003100504,0.01042153,0.0004020931,0.0005234571,0.0001838358,0.008914015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.719997,"threshold_uncertainty_score":0.8831313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2729485310152599,"score_gpt":0.5450953399739819,"score_spread":0.272146808958722,"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."}}