{"id":"W2014381904","doi":"10.1021/pr1008652","title":"<i>In Silico</i> Protein Interaction Analysis Using the Global Proteome Machine Database","year":2010,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"In silico; Proteome; Protein–protein interaction; Computational biology; Proteomics; Human proteome project; Immunoprecipitation; Histone; Biology; Cell biology; Database; Bioinformatics; Biochemistry; Computer science; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002905471,0.0001699815,0.0003201917,0.0003927408,0.0002736524,0.0001345884,0.0008647286,0.0001503492,0.0005439346],"category_scores_gemma":[0.0006961471,0.0001185468,0.0002121948,0.002030476,0.0002031253,0.0005236057,0.0002675949,0.002866593,0.000008421353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003612489,"about_ca_system_score_gemma":0.000316467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003746229,"about_ca_topic_score_gemma":0.0001869899,"domain_scores_codex":[0.9975256,0.0001389248,0.0007098375,0.0002855038,0.0008787028,0.0004613959],"domain_scores_gemma":[0.9979053,0.0001056071,0.0004693163,0.0007250939,0.0005888588,0.000205892],"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":[0.0001734369,0.0001908647,0.001595103,0.0000631397,0.00008431468,0.00003433526,0.00004329054,0.0001542931,0.9962293,0.0008502484,0.00005034485,0.0005313312],"study_design_scores_gemma":[0.0006330531,0.00009485374,0.0001023163,0.0001655995,0.00008274514,0.0002791266,0.0002150785,0.01012251,0.9656536,0.01337588,0.009038044,0.0002372264],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681714,0.00008602063,0.02669816,0.002831125,0.00003684368,0.001179789,0.00007088337,0.00002259369,0.0009032095],"genre_scores_gemma":[0.9141732,0.00002997472,0.08468372,0.00003067884,0.0004108913,0.0003664201,0.000008440606,0.0000251891,0.0002714256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05798556,"threshold_uncertainty_score":0.9994338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05593105439590974,"score_gpt":0.4378471501177121,"score_spread":0.3819160957218024,"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."}}