{"id":"W2110150937","doi":"10.1186/1748-7188-5-34","title":"Predicting direct protein interactions from affinity purification mass spectrometry data","year":2010,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Probabilistic logic; Set (abstract data type); Identification (biology); Data mining; Algorithm; Sensitivity (control systems); Graph; Artificial intelligence; Theoretical computer science; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003481814,0.0002034575,0.0001955726,0.00006229613,0.0001397708,0.00004343221,0.0006356817,0.0003118159,0.00003154217],"category_scores_gemma":[0.0002939393,0.0001994922,0.00009093255,0.0001201404,0.0001052673,0.0000113374,0.0002660783,0.000289788,0.00001279005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001055166,"about_ca_system_score_gemma":0.00007977833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009195634,"about_ca_topic_score_gemma":0.000117556,"domain_scores_codex":[0.9986283,0.00005145751,0.0003355609,0.0005766086,0.0000655129,0.0003425311],"domain_scores_gemma":[0.9984078,0.00003918165,0.0001913603,0.001148995,0.0001151672,0.00009744681],"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":[0.00003643183,0.00004378321,0.0003934265,0.000006269272,0.0001384926,7.635868e-7,0.00001235847,0.00000686773,0.987923,0.0007086435,0.0003518777,0.01037809],"study_design_scores_gemma":[0.001215178,0.0004855383,0.0006705334,0.00001702077,0.0001173905,0.00002253699,0.00006250875,0.02033755,0.8595911,0.01563384,0.1011643,0.0006825861],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3638553,0.0002864951,0.6312839,0.0003177827,0.0009591528,0.0006935066,0.001227362,0.00004284541,0.001333626],"genre_scores_gemma":[0.8124822,0.00002109504,0.1776136,0.0001801455,0.0008872909,0.0001224785,0.0084959,0.00003691818,0.0001603937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4536704,"threshold_uncertainty_score":0.8135056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173378879299586,"score_gpt":0.2870760176882253,"score_spread":0.2697381297582667,"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."}}